%Created by Franz Rothlauf 17 August 2005
%WBL 31 Aug 2005
%extracted by gecco2005_proc.awk $Revision: 1.00 $ http://www.cs.essex.ac.uk/staff/W.Langdon/
%created from gecco2005lbp.abstracts
%created from gecco2005lbp.bib

%WBL  1 Aug 2017 ensure passes bibclean v3.02

@Proceedings(rothlauf:2005:GECCO:lbp,
  title = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  keywords = {genetic algorithms, genetic programming},
  notes = {Distributed on CD-ROM at GECCO-2005},
  keywords = {genetic algorithms, genetic programming, AIS, ACO, GEP, DE, PSO, UMDA, datamining, EHW},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/},
  notes = {Distributed on CD-ROM at GECCO-2005},
)


@InProceedings(Hauptman:gecco05lbp,
  author = {Ami Hauptman and Moshe Sipper},
  title = {Analyzing the Intelligence of a Genetically Programmed Chess Player},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/21-hauptmann.pdf},
  keywords = {genetic algorithms, genetic programming},
  abstract = {We investigate a strong chess endgame player, previously evolved by us through genetic programming [1]. Its performance is analysed across four games, demonstrating the chess-playing capabilities developed through evolution. We end with a discussion of our GP-evolved player\'s pros and 
cons},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Shervais:gecco05lbp,
  author = {Stephen Shervais},
  title = {System Identification Using Off-Optimum Data From A Genetic Algorithm},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/22-shervais.pdf},
  abstract = {When developing an artificial neural net model of a system, the most efficient way to obtain training and test data is often to generate a large set of random inputs and run them through the model.   But that is not the only way to do it.  We demonstrate the use of genetic algorithm-generated data as a source of input-output pairs for training an artificial neural network.  If the genetic algorithm and neural network are being developed together - for example, to provide system identification in support of a control system - this data is readily available and performs as well as a random search of the state space},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Reis:gecco05lbp,
  author = {Cecilia Reis and Tenreiro Machado and Boaventura  Cunha},
  title = {Digital Circuit Design Using Dynamic Fitness Functions},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/23-reis.pdf},
  abstract = {We propose and analyses the performance of a Genetic Algorithm using two new concepts, namely a static fitness function including a discontinuity measure and a fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. In both cases, experiments reveal superior results in terms of speed and convergence to achieve a solution},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(De:gecco05lbp,
  author = {Debray De and Sonai Rai and Amit Konar and Amita Chatterjee},
  title = {A Fuzzy Logic Controller Based Dynamic Routing Algorithm with SPDE based Differential Evolution Approach},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/26-debray.pdf},
  abstract = {We propose a novel scheme of Fuzzy Logic based dynamic routing in computer networks. The proposed dynamic routing algorithm is suited for application in Interior Gateway Protocols (IGP) inside an autonomous system (AS), such as a Local Area Networks (LAN). It is used in the protocols to find out a set of best possible routes, where each of the nodes broadcasts link status rather than broadcasting the whole routing table. The Self Adaptive Pareto Differential Evolution (SPDE) algorithm is little modified to apply it in solving efficient and optimal dynamic routing problem. One of the main features of the proposed routing scheme is that it outputs hierarchical quality solutions so that, if one path is blocked, there will be provisions of alternative paths for successful packet transmission in computer networks. The architecture of the proposed dynamic routing scheme is mainly composed of a controller, which makes use of a fuzzy-based decision making system. In a real world dynamic environment the controller finds out the optimal policy that determines weights on the parameters of routing. The total dynamic routing scheme is made to evolve intelligently in changing characteristics of daily network loads and usages. The proposed algorithm grains out the optimal routes for the packets to be transmitted. The paper also reviews the overall performance of the proposed routing scheme by applying it to a number of randomly generated real time computer networks. The fast response of our proposed scheme makes it suitable for real world applications like dynamic routing},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(De:gecco05lbp,
  author = {Debray De and Sonai Rai and Amit Konar and Amita Chatterjee},
  title = {An Evolutionary SPDE breeding based Hybrid Particle Swarm Optimizer: Application in Coordination of Robot Ants for Camera Coverage Area Optimization},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/27-debray.pdf},
  abstract = {We propose a new Hybrid Particle Swarm Optimiser model based on breeding concepts from novel evolutionary algorithms. The hybrid PSO combines traditional velocity and position update rules of RANDIW-PSO and ideas from Self Adaptive Pareto Differential Evolution Algorithm (SPDE). The hybrid model is tested and compared with some high quality PSO models like the RANDIW-PSO and TVIW-PSO. The results indicate two good prospects of our proposed hybrid PSO model: potential to achieve faster convergence as well as potential to find a better solution. We obtain outstanding performance on solving single objective problems in comparison to several other PSO models. The hybrid PSO model, with the abovementioned features, is then efficiently used in the problem of coordinating a system of robot ants in order to help them to probe as much camera coverage area of some planetary surface or working field as possible, with minimum common area coverage. Thus by the Hybrid PSO, the total intersecting area of camera coverage regions of the robots is minimised quickly and efficiently, which is suited for online real world applications},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Grahl:gecco05lbp,
  author = {J{\"o}rn Grahl and Stefan Minner and Franz Rothlauf},
  title = {An analysis of iterated density estimation and sampling in the UMDAc algorithm},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/30-grahl.pdf},
  abstract = {Of late, much progress has been made in developing Estimation of Distribution Algorithms (EDA), algorithms that use probabilistic modelling of high quality solutions to guide their search. While experimental results on EDA behaviour are widely available, theoretical results are still rare. This is especially the case for continuous EDA. We develop theory that predicts the effect of iterated density estimation and sampling used in the Univariate Marginal Distribution Algorithm in the continuous domain (UMDAc). We use truncation selection and monotonous fitness functions. Monotonous functions are commonly used to model the algorithm behaviour far from the optimum. Our result includes formulae to predict population statistics in a specific generation as well as population statistics after convergence. We find that population statistics develop identically for monotonous functions. We show that if assuming monotonous fitness functions, the distance that UMDAc travels across the search space is bounded and solely relies on the percentage of selected individuals and not on the structure of the fitness landscape. This can be problematic if this distance is too small for the algorithm to find the optimum. Also, by wrongly setting the selection intensity, one might not be able to explore the whole search space},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Geem:gecco05lbp,
  author = {Zong Geem},
  title = {School Bus Routing using Harmony Search},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/31-zong.pdf},
  abstract = {A recently-developed nature-inspired algorithm, Harmony Search, mimicking music improvisation, is introduced and applied to transportation problem (school bus routing), and compared with popular evolutionary algorithm (genetic algorithm). The Harmony Search is  using the musical process of searching for a perfect state of harmony. This algorithm was applied to a test network consisting of one bus depot, one school and ten bus stops with demand by commuting students. This school bus routing example is a multi-objective problem to minimise the number of operating buses and also the travel time of all buses, with bus capacity and time window constraints that are considered as penalty costs. Harmony Search could find good solution within the reasonable time with other advantages such as no derivative requirement and no initial value assumption. The presented routing model is expected to be applied to large-scale real networks in the future},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings( Yao:gecco05lbp,
  author = {Meng Yao and Chun-Ni Dai and Min Pei and Zhu-Jie Xie and Chun-Hong Chen},
  title = {The Species Compete-Die out (SCD) Algorithms Model for Evolutionary Computation},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/32-yao.pdf},
  keywords = {Species Compete-Die out Algorithms, Evolutionary Algorithms, premature convergence, optimisation},
  abstract = {Inspired by the phenomena of evolution in nature and society, we put forward a new model for evolutionary algorithms to conquer its limitation of premature convergence. Then the model is used to optimise the parameters of model of crop in greenhouse and compared the result to that of the simple Evolutionary Algorithms under the same situation. Finally it is convinced that the new model is superior in stability and astringency.},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Voss:gecco05lbp,
  author = {Mark Voss},
  title = {The Principal Component Particle Swarm Optimization (PCPSO)},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/33-voss.pdf},
  abstract = {Particle Swarm Optimisation (PSO) is based on the notion of particles flying through solution space. Each particle is assumed to have 
n-dimensions that are mapped to the variables of the function that is being evaluated. The standard PSO algorithm updates a particle\'s position by moving towards the particle\'s past personal best and the best particle that has been found. We introduce the Principal Component Particle Swarm Optimization (PCPSO) procedure. The Principal Component Particle Swarm
Optimisation procedure flies the particles in two separates spaces at the same time; the traditional n-dimensional x space and a
rotated m-dimensional z space where m<=n},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Pena:gecco05lbp,
  author = {Alejadro {Pe{\~n}a P.} and Jesus Antonio {Hernandez R.}},
  title = {Interpolation and Exploration of Response Surfaces using Evolutionary NURBS, LaGrange Constraint and Cylindrical Operators},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/35-pena.pdf},
  abstract = {A computational model for the interpolation and exploration of Complex Response Surfaces is described and analysed. This computational model consists of two stages: an initial stage in which a group of measured points is interpolated by means of the coalition of characteristic concepts of vector geometry, numeric methods and evolutionary computation to construct a response surface; and a second stage, where a series of good trajectories by means of the exploration of the interpolated surface are determined. In this stage, an evolutionary algorithm, processing a mutation operator that incorporates the fundamental concepts of the cylindrical coordinates, is used to identify a trajectory containing the best combinations among the variables of the particular process this surface represents},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Bentley:gecco05lbp,
  author = {Peter Bentley and Julie Greensmith and Supiya Ujjin},
  title = {Two Ways to Grow Tissue for Artificial Immune Systems},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/37-bentley.pdf},
  abstract = {An immune system without tissue is like evolution without genes. Something very important is missing. Here we present the novel concept of tissue for artificial immune systems. Much like the genetic representation of genetic algorithms, tissue provides an interface between problem and immune algorithm. Two tissue-growing algorithms are presented with experimental results illustrating their abilities to dynamically cluster data and provide useful signals. The use of tissue to provide an innate immune response driving the adaptive response of conventional immune algorithms is then discussed},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Gomes:gecco05lbp,
  author = {Alvaro Gomes and C. Henggeler Antunes and A. Gomes Martins},
  title = {Design of an Adaptive Mutation Operator in an Electrical Load Management Case Study},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/38-gomes.pdf},
  abstract = {An adequately designed and parameterised set of operators is crucial for an efficient behaviour of Genetic Algorithms (GAs). In GAs the mutation operator is commonly used with fixed mutation rates. However, in nature some genes mutate more often than others and mutation rates can be influenced by the environment. a comparative analysis of the effects of using an adaptive mutation operator is presented in the operational framework of a multiobjective GA to design and select electrical load management strategies. It is shown that the use of a time/space varying mutation operator increases the efficiency and the efficacy of the algorithm},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Gargano:gecco05lbp,
  author = {Michael Gargano and Kasinadhuni Maheswaraprasad},
  title = {Rank aggregation for metasearch engines using a self-adaptating genetic algorithm with multiple genomic representations},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/39-gargano.pdf},
  abstract = {We consider the problem of combining rankings from the findings of various search engines in order to select documents based on differing and multiple criteria thus improving the results of a search.  We propose using multiple genomic redundant representations in a self-adapting genetic algorithm (GA) employing various codes with different locality properties. These encoding schemes insure feasibility after performing the operations of crossover and mutation and also ensure the feasibility of the initial randomly generated population (i.e., generation 0).  The GAs applied in solving this NP hard problem employ non-locality or locality representations when appropriate (i.e., the GA adapts to its current search needs) which makes the GAs more efficient},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Suda:gecco05lbp,
  author = {Tatsuya Suda and Michael Moore and Tadashi Nakano and Ryota Egashira and Akihiro Enomoto},
  title = {Exploratory Research on Molecular Communication between Nanomachines},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/40-suda.pdf},
  abstract = {This is a position paper about our exploratory research on molecular communication between nanomachines (e.g., small scale devices): a new communication paradigm using chemical molecules as communication carriers. This paper presents our first attempt to design a framework for describing such a communication paradigm. Molecular communication is based on the observation that existing biological systems use molecules as communication carriers. Molecular communication is designed using components existing in biological systems (e.g., receptors, nano-scale reactions, communication molecules) to enable communication at the molecular scale in an aqueous medium. We believe that with the advancement of bionanotechnology (as demonstrated by the work of Dr. Drew Endy, who gives a keynote speech at this year’s GECCO,) it may become possible to use components in biological systems to implement molecular communication systems. This position paper presents the concept of molecular communication and briefly introduces our attempt toward creating molecular communication systems},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Vlcek:gecco05lbp,
  author = {Martin Vlcek},
  title = {Is the Economy a living object?},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/41-vlcek.pdf},
  abstract = {I try to prove that the model used in this presentation opens up a new perspective on the economy. This perspective could indicate the economy as being an object with living properties.
The model uses two criteria for learning: tension and satisfaction. Tension is the inaccuracy of forecasting and this tension is minimised. Satisfaction is the mutual balance of coefficients used for this forecasting.
The model proceeds in an analogical way to living objects: it solves the tension moving according to the gradient of the satisfaction.
A living object, besides its movement as a whole on the gradient of satisfaction, is characterised by typical parts with a typical behaviour. The activity of these parts obeys some rules of consequences, for example: there first occurs a tension which is followed by a reaction in the muscular system which will change its form etc. If we are successful in locating these consequences in the economy it will be another brick in the wall that solidifies its definition as a living object. 
It seems that fixed capital formation corresponds to the muscular system and consumption of households is a candidate for the nervous system},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Hu:gecco05lbp,
  author = {Jiaqiao Hu and Michael Fu and Steven Marcus},
  title = {Model Reference Adaptive Search: A New Approach to Global Optimization},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/42-hu.pdf},
  abstract = {We present a randomised algorithm called Model Reference Adaptive Search (MRAS) for solving global optimisation problems. The algorithm generates at each iteration a group 
of candidate solutions according to a parameterised probabilistic model. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be biased toward the region containing high quality solutions. The parameter updating procedure in MRAS is guided by a sequence of implicit reference models that will eventually converge to a model producing only the optimal
solutions. We establish global convergence of MRAS in both continuous and combinatorial domains.
Numerical studies are also carried out to demonstrate the effectiveness of the algorithm.},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Rosenberg:gecco05lbp,
  author = {Brad Rosenberg and Janet Burge and Paul Gonsalves},
  title = {Applying Evolutionary Multi-Objective Optimization to Mission Planning for Time-Sensitive Targets},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/43-rosenberg.pdf},
  abstract = {We describe an approach to air campaign mission planning using evolutionary multi-objective optimisation. With the landscape of warfare constantly changing, timely and accurate employment of air assets for military operations has become even more crucial. Of particular importance is in addressing time-sensitive and time-critical targets. Such operations require a rapid search of potential mission plans to evaluate their ability on an array of objectives. This type of system design problem, consisting of a large solution space and complicated fitness landscape, has proved to be approached successfully using evolutionary algorithms. Additionally, the presence of potentially multiple conflicting objectives lends to the suitability of using multi-objective optimization techniques. We describe our preliminary experiments using both aggregation and dominance-based approaches to evolutionary multi-objective optimisation for addressing time-sensitive/critical targets},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Lopez:gecco05lbp,
  author = {Edgar {Galvan Lopez} and Katya {Rodriguez Vazquez} and Riccardo Poli},
  title = {Beneficial Aspects of Neutrality in GP},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/45-lopez.pdf},
  keywords = {genetic algorithms, genetic programming, EHW},
  abstract = {We propose a new approach, called Multiple Outputs in a Single Tree (MOST), to Genetic Programming. The idea of this approach is to specify explicitly Neutrality and study how this improves the evolutionary process. For this sake, we have used several evolvable hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Skalny:gecco05lbp,
  author = {Matthew Skalny and Jim Overholt and Greg Hudas and Graham Fiorani},
  title = {Genetic Algorithm Strategies for Voronoi Classifier Navigation},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/47-skalny.pdf},
  abstract = {There are many approaches to guiding robots in partially known environments, including waypoints, D*, and various other methods. We describe a new method for robot navigation that uses navigation “beacons” called Voronoi classifiers to guide a robot to a goal area, and the application of a genetic algorithm for optimising the placement of these classifiers. Our results show that a genetic algorithm (GA) can be a good way of placing the classifiers},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Lee:gecco05lbp,
  author = {Seungwon Lee and Paul {von Allmen} and Wolfgang Fink and Anastassios E. Petropoulos and Richard J. Terrile},
  title = {Comparison of Multi-Objective Genetic Algorithms in Optimizing Q-Law Low-Thrust Orbit Transfers},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/48-lee.pdf},
  abstract = {Multi-objective genetic algorithms (MOGA) are used to optimise a low-thrust spacecraft control law for orbit transfers around a central body. A Lyapunov feedback control law called the Q-law is used to create a feasible orbit transfer. Then, the parameters in the Q-law are optimized with MOGAs. The optimisation goal is to minimise both the flight time and the consumed propellant mass of the trajectory created by the Q-law, and consequently to find Pareto-optimal trajectories. To improve the qualities of the obtained Pareto-optimal solutions, elitism and a diversity-preservation mechanism are incorporated into MOGA. The MOGA performance with and without the new mechanisms are systematically compared and evaluated with quantitative metrics. The new mechanisms significantly improve the convergence and distribution of the resulting Pareto front for the low-thrust orbit-transfer optimization problem. The new mechanisms also improve the statistical stability and the computational efficiency of the algorithm performance},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(tanev:2005:GECCOLB,
  author = {Ivan Tanev and Michal Joachimczak and Hitoshi Hemmi and Katsunori Shimohara},
  title = {Evolving Driving Agent for Remote Control of Scaled Model of  a Car},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/50-tanev.pdf},
  keywords = {genetic algorithms, genetic programming},
  abstract = {We present an approach for automatic design via genetic programming of the functionality of driving agent, able to remotely operate a scale model of a car running in a fastest possible way. The agent’s actions are conveyed to the car via standard radio control transmitter. The agent perceives the environment from a live video feedback of an overhead camera. In order to cope with the inherent video feed latency we propose an approach of anticipatory modelling in which the agent considers its current actions based on anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. The driving style of the agent is first evolved offline on a software simulator of the car and then adapted online to the real world.  Experimental results demonstrate that on long runs the agent's-operated car is only marginally (about 5%) slower than a human-operated one, while the consistence of lap times posted by the evolved driving agent is better than that of a human. Presented work can be viewed as a step towards the development of a framework for automated design of the controllers of remotely operated vehicles capable to find an optimal solution to various tasks in different traffic situations and road conditions},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Wiese:gecco05lbp,
  author = {Kay C. Wiese and Andrew Hendriks and Alain Deschenes and Belgacem {Ben Youssef}},
  title = {The Impact of Pseudorandom Number Quality on P-RnaPredict, a Parallel Genetic Algorithm for RNA Secondary Structure Prediction},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/52-wiese.pdf},
  abstract = {We present a parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA\'s performance. The three generators tested are the C standard library PRNG RAND, a parallelised multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented. The PRNG comparison tests were performed with known structures that are 118, 122, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Kamalian:gecco05lbp,
  author = {Raffi Kamalian and Ying Zhang and Hideyuki Takagi and Alice M. Agogino},
  title = {Reduced Human Fatigue Interactive Evolutionary Computation for Micromachine Design},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/53-kamalian.pdf},
  abstract = {We present a newly developed novel method of using Interactive Evolutionary Computation (IEC) for the design of Microelectromechanical Systems (MEMS). A key limitation of IEC is human fatigue. Based on the results of a study of a previous IEC MEMS tool, an alternate form that requires less human interaction is presented. The method is applied on top of a conventional multi-objective genetic algorithm, with the human in a supervisory role, providing evaluation only every nth-generation. Human interaction is applied to the evolution process by means of Pareto-rank shifting, which is used for the fitness calculation used in selection. Results of a test of 13 users shows that this IEC method can produce statistically significant better MEMS resonators than non-interactive evolutionary synthesis},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Parent:gecco05lbp,
  author = {Johan Parent and Annie Nowe and Anne Defaweux},
  title = {Addressing the Even-n-parity problem using Compressed Linear Genetic Programming},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/54-parent.pdf},
  keywords = {genetic algorithms, genetic programming},
  abstract = {Compressed Linear Genetic Programming (cl-GP) uses substring compression as a modularisation scheme. Despite the fact that the compression of substrings assumes a tight linkage between alleles, this approach improves the GP search process. The compression of the genotype, which is a form of linkage learning, provides both a protection mechanism and a form of genetic code reuse. This text presents the results obtained with the cl-GP on the Even-n-parity problem. Results indicate that the modularization of the cl-GP performs better than a normal l-GP as it allows the cl-GP to preserve useful gene combinations. Additionally the cl-GP modularisation is well suited for problems where the problem size is adjusted in a co-evolutionary setup, the problem size increases each time a solution is found},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Fogarty:gecco05lbp,
  author = {Terence Claus Fogarty},
  title = {Automatic Concept Evolution (ACE)},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/55-fogarty.pdf},
  abstract = {An evolutionary algorithm can be used by a distributed group of interacting people to produce the solution to a problem. First the problem must be defined. Each member of the group then suggests solutions to the problem. They go on to improve these suggestions by mutation and crossover. They show their preferences by replication and can throw in new suggestions if they think of them. An experiment involving seven people using this algorithm to choose a name for it will be described. The successes and limitations of this experiment will be discussed. A more experiment will be proposed},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Hammond:gecco05lbp,
  author = {Michelle Okaley Hammond and Terence Claus Fogarty},
  title = {Co-operative OuLiPian Generative Literature using Human Based Evolutionary Computing},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/56-hammond.pdf},
  abstract = {The tools and techniques employed by Interactive Evolutionary Computing [21] offer procedures which put human interaction at the centre of the problem solving process.  Human Based GA systems [7], [8] such as the Automatic Concept Evolver (ACE) [6] offer an infrastructure that has been proven to work efficiently in solving problems which involve the evolution of natural language strings. Oulipo is a French literary movement founded in the 1960s which applies mathematical constraints in the creation of literature making them distinct from other experimental literary groups [10]. Their literary creations change the dynamics of the author-reader relationship because they provide a different experience from reading traditional narratives where intuition and emergence are fundamental to creative exploration. We propose the application of the ACE methodology to arbitrate between a group of interacting authors to produce Oulipian literature and specifies experiment to test this approach in practice. It is proposed, that ACE can be likened to an Oulipian constraint and thus the experimental output will allow the reader to choose a path through the narrative, exploring new meaning},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Karaoglu:gecco05lbp,
  author = {Nihat Karaoglu and Bernard Manderick},
  title = {FAPSTER - A Genetic Algorithm for Frequency Assignment Problem},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/57-karaoglu.pdf},
  abstract = {We describe an efficient Genetic Algorithm (GA) for solving the minimum interference frequency assignment problem (MI-FAP) using a new problem representation. The GA we present looks for the best assignment of a limited number of frequencies  to a set of stations minimising the total disturbance caused by stations operating at the same frequencies. We show that our problem representation based on permutation encoding and clustering gives better results in comparison with the existing problem representations in the literature. Our problem representation reduces the search space from  f^n to  (f!)^(n/f) and improves the time complexity of the fitness function from O(n^2) to O(n^2/f) where f  is the number of frequencies and n is the number of stations. We compare the performance of our algorithm with the algorithms using other problem representations and confirm our results on a real-world problem},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Kiehl:gecco05lbp,
  author = {Thomas Kiehl and Bethany Hoogs and Christina LaComb and Deniz Senturk},
  title = {Evolving Multi-Variate Time-Series Patterns for the Discrimination of Fraudulent Financial Filings},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/58-kiehl.pdf},
  abstract = {We consider an application of evolutionary computation (EC) to classification and pattern discovery.  In particular we present a genetic algorithm (GA) used to discriminate cases of potential financial statement fraud.  Of key interest to us is the ability to distinguish multidimensional patterns over time.   The GA evolves strings over a pattern definition language to define class boundaries and to select classification features.  The language defined allows for 1) the integration of data across time and across a number of variables 2) the integration of quantitative as well as qualitative data 3) the direct evolution by genetic algorithm and 4) easy interpretation by human experts.  The data and method are described and results presented.  Results offer a 63% true positive rate with a false positive rate of 5%.  These results compare favourably with other published results on comparable data. Our technique captures behaviours not evident from traditional data analysis methods.   The output from our system has the additional benefit of being easily understood and used by experts and practitioners in the field.   This makes our approach more desirable than other black-box solutions. These techniques provide a foundation for multidimensional behavior analysis of data from a variety of domains including, financial, biological, manufacturing and clinical},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Haas:gecco05lbp,
  author = {Wolfgang Haas and Sheridan Houghten},
  title = {Evolutionary Algorithms for Optimal Error-Correcting Codes},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/59-haas.pdf},
  abstract = {The maximum possible number of codewords in a q-ary code of length n and minimum distance d is denoted Aq(n,d). It is a fundamental problem in coding theory to determine this value for given parameters q, n and d. Codes that attain the maximum are said to be optimal. Unfortunately, for many different values of these parameters, the maximum number of codewords is currently unknown: instead we have a known upper bound and a known lower bound for this value. 

We investigate the use of different evolutionary algorithms for improving lower bounds for given parameters. We relate this problem to the well-known Maximum Clique Problem. We compare the performance of the evolutionary algorithms to Hill Climbing, Beam Search, Simulated Annealing, and greedy methods. We found that the GAs outperformed all other algorithms in general; furthermore, the difference in performance became more significant when considering harder test cases},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Gang:gecco05lbp,
  author = {Peng Gang and Takeshi Nakatsuru and Shigeru Nakayama},
  title = {Discussions on LGA with Parallel System},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/61-gang.pdf},
  abstract = {We discuss a parallel genetic algorithm (PGA) which focuses on the local operator for Travelling salesman problem (TSP). The local operator is a simple GA named as Local Genetic Algorithm (LGA). The LGA is combined to another GA named as Global Genetic Algorithm (GGA). It increases the computational time running a GA as a local operator in another one. To solve this problem, we build a parallel system based on our previous works for running the LGA to speed up the process. The results show that LGA improve the search quality significantly and it is more efficient running LGA with parallel system than single CPU},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Fries:gecco05lbp,
  author = {Terrence Fries},
  title = {Autonomous Robot Motion Planning in Diverse Terrain Using Genetic Algorithms},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/62-fries.pdf},
  abstract = {Optimal motion planning is critical for the successful operation of an autonomous mobile robot.  Many proposed approaches use either fuzzy logic or genetic algorithms (GAs), however, most approaches offer only path planning or only trajectory planning, but not both.  In addition, few approaches attempt to address the impact of varying terrain conditions on the optimal path.  This paper presents a fuzzy-genetic approach that provides both path and trajectory planning, and has the advantage of considering diverse terrain conditions when determining the optimal path.  The terrain conditions are modelled using fuzzy linguistic variables to allow for the imprecision and uncertainty of the terrain data.  Although a number of methods have been proposed using GAs, few are appropriate for a dynamic environment or provide response in real-time.  The method proposed is robust, allowing the robot to adapt to dynamic conditions in the environment},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Mehr:gecco05lbp,
  author = {Ali Farhang Mehr},
  title = {Comparative Study of Several Multi-Objective Genetic Algorithms},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/64-farhang.pdf},
  abstract = {We investigate the relative performance of several well-known Multi-Objective GA’s (MOGA’s) on a quantitative and objective basis. Two aspects of MOGA performance are studied and compared: 1) convergence rate to the Pareto frontier; and 2) diversity of solutions. Two quantitative measures (i.e. quality indices) are selected accordingly, each addressing one aspect of performance. To account for the stochastic nature of GA’s, multiple runs are performed for each MOGA to create samples of solution sets for statistical comparison. Based on the results of this study, we will observe several characteristics of the tested MOGA’s, which can be helpful in selecting an appropriate MOGA for a given multi-objective optimisation task},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Tchernev:gecco05lbp,
  author = {Elko Tchernev and Dhananjay Phatak},
  title = {Queue-based Genetic Programming},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/65-tchernev.pdf},
  keywords = {genetic algorithms, genetic programming},
  abstract = {We describe the use of a queue instead of a stack or a parse tree for the internal representation and genetic operations of a Genetic Programming system.  Specifically, implementation issues and application areas are discussed},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Banks:gecco05lbp,
  author = {Edwin Roger Banks and Edwin Nunez and Paul Agarwal and Claudette Owens and Marshall McBride and Ron Liedel},
  title = {Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO)},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/66-banks.pdf},
  keywords = {genetic algorithms, genetic programming},
  abstract = {According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively searching for improved sensor methodologies to detect and discriminate buried UXO from other objects. We describe the results of work performed on data gathered by the GeoPhex GEM-3 electromagnetic sensor during their attempts to discriminate buried UXO at the U.S. Army Jefferson Proving Ground (JPG). We used a variety of evolutionary computing (EC) approaches that included genetic programming, genetic algorithms, and decision-tree methods. All approaches were essentially formulated as regression problems whereby the EC algorithms used sensor data to evolve buried UXO discrimination chromosomes. Predictions were then compared with a ground-truth file and the number of false positives and negatives determined},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Yu:gecco05lbp,
  author = {Han Yu and Annie Wu},
  title = {An Incremental Approach to the Proportional GA},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/67-yu.pdf},
  abstract = {The Proportional Genetic Algorithm (PGA) supports truly location independent solution encoding. We propose a novel approach, called incremental building blocks, to improve the search performance of PGA. The main idea of this approach is to evolve low level building blocks individually in the beginning of a run and then continuously select and combine good building blocks to form larger and more complete ones until an optimal solution is found. Empirical studies in the resource allocation domain show that this approach is able to improve both the search quality and efficiency of a PGA. Further analysis of experimental data reveals that this approach is better at preserving the self-similarity of chromosomes during evolution},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Hilton:gecco05lbp,
  author = {Amy Chan Hilton and Yuanhai Li},
  title = {Optimal groundwater sampling network design through ant colony optimization},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/68-hilton.pdf},
  abstract = {Groundwater long-term monitoring (LTM) is required to assess the performance of groundwater remediation and human being health risk at post-closure sites where groundwater contaminants are still present. The large number of sampling locations, number of constituents to be monitored, and the frequency of the sampling make the LTM costly, especially since LTM may be required over several decades.  An optimisation algorithm based on the ant colony optimization (ACO) paradigm for solving the travelling salesman problem (TSP) is proposed to reduce the number of monitoring wells while minimising the overall data loss due to fewer sampling locations.  The ACO method is inspired by the ability of ant colony to identify the shortest route between their nest and a food source. Ants depositing pheromones along their paths act as a form of indirect communication.  The developed ACO-LTM algorithm is applied to a field site with an existing 30-well LTM network.   Optimal LTM networks with 27 to 21 wells, which represent a 10% to 30% reduction in sampling locations, resulted in overall data losses ranging from 0.383 to 1.74.  Results from developed ACO-LTM algorithm provide a proof-of-concept for the application of the general ACO analogy to the groundwater LTM sampling location optimisation problem},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(MacNish:gecco05lbp,
  author = {Cara MacNish},
  title = {Benchmarking Evolutionary Algorithms: The Huygens Suite},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/69-macnish.pdf},
  abstract = {The success (and, importantly, potential success) of randomised population-based algorithms and their hybrids on ever more difficult optimisation problems has led to an explosion in the number of algorithms and variants proposed. It is difficult to definitively compare the range of algorithms proposed, and therefore to advance the field.

We discuss the difficulties of providing widely available benchmarking, and present a solution that addresses these difficulties. Our solution uses automatically generated fractal landscapes, and allows user’s algorithms written in any language and run on any platform to be “plugged into” the benchmarking software via the web},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(James:gecco05lbp,
  author = {Derek James and Philip Tucker},
  title = {Evolving a Neural Network Active Vision System for Shape Discrimination},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/70-james.pdf},
  abstract = {Previous research has demonstrated the potential for neural network controlled active vision systems to solve shape discrimination and object recognition tasks.  However, this approach has not been very well explored, and previous implementations of such systems have been somewhat limited in scope.  We present an evolved neural network based active vision system that is able to move about a 2D surface in any direction, along with the ability to zoom and rotate.  We demonstrate that a system with such features can correctly classify shapes presented to it, despite variance in location, scale, and rotation.  And, contrary to our initial assumptions, effective discrimination is actually improved when the ability to rotate is disabled},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Schmidt:gecco05lbp,
  author = {Michael Schmidt and Hod Lipson},
  title = {Co-evolution of Fitness Maximizers and Fitness Predictors},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/71-schmidt.pdf},
  abstract = {We introduce an estimation of distribution algorithm (EDA) based on co-evolution of fitness maximisers and fitness predictors for improving the performance of evolutionary search when evaluations are prohibitively expensive. Fitness predictors are lightweight objects which, given an evolving individual, heuristically approximate the true fitness. The predictors are trained by their ability to correctly differentiate between good and bad solutions using reduced computation. We apply co-evolving fitness prediction to symbolic regression and measure its impact. Our results show that the additional computational investment in training the co-evolving fitness predictors can greatly enhance both speed and convergence of individual solutions while overall reducing the number of evaluations. In application to symbolic regression, the advantage of using fitness predictors grows as the complexity of models increases},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(vanBreugel:gecco05lbp,
  author = {Floris {van Breugel} and Hod Lipson},
  title = {Evolving Buildable Flapping Ornithopters},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/72-vanbreugel.pdf},
  abstract = {We present a process of evolving flapping flight control patterns for an ornithopter. We focus both on generating the flapping motion pattern as well as a realistic kinematic mechanism that can generate this motion in practice. The ultimate goal of the project is to translate the simulated models and behaviour into a realized physical model capable of independent untethered flight},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(DiPenta:gecco05lbp,
  author = {Massimiliano {Di Penta} and Luigi Troiano},
  title = {Using Fuzzy Logic to Relax Constraints in GA-Based Service Composition},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/73-dipenta.pdf},
  abstract = {The  rapid  diffusion  of   web  services  is  changing  the  software engineering landscape.   One of the most  interesting features offered
by  service-oriented systems  is the  possibility to  perform dynamic binding,  i.e.   choosing,   among  sets  of  semantically  equivalent services,  those  which better  contribute  to  meet some  constraints (e.g.,  related  to  the cost  or  to  any  other Quality  of  Service
attributes)  and  optimise some  other  criteria  (e.g., the  response time).  Solving this problem is  NP-hard, and approaches to tackle it
using Genetic Algorithms have been proposed.

In some cases, especially when it is not possible to find any solution to  the   aforementioned  problem,  it   would  be  useful   to  relax
constraints, in  order to find some alternative  solutions that, while not  meeting the  initial  constraints, at  least  offer a  reasonable
Quality  of Service. We propose  the use  of fuzzy  logic to address the imprecision in  specifying QoS constraints, estimating QoS
values and expressing Service Level Agreements},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Studer:gecco05lbp,
  author = {Greg Studer and Hod Lipson},
  title = {Spontaneous emergence of self-replicating, competing cube species in physical cube automata},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/74-studer.pdf},
  abstract = {We propose and implement a new variant of cellular automata incorporating physical rules, based largely on the nonuniform cellular automata type first investigated by Sipper [10].  The new automata rules are designed to be realizable in an actual electromechanical robotic module called a molecube, which was built by us [6][14] and shown empirically to have self-replicating ability.  The spontaneous and continuous emergence of simple self-replication is demonstrated using this automata platform.  In addition, qualitative differences are observed between global state mutation and reproductive mutation},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Koduru:gecco05lbp,
  author = {Praveen Koduru and Ashish Ahuja and Kyle McDowell and Lukas Lansky and Sanjoy Das and Stephen Welch},
  title = {The Multi-objective Evolution of Mobile Robot Behavior},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/75-koduru.pdf},
  abstract = {Autonomous mobile robot navigation is a complex problem and evolutionary principles applied to such problems provide good solutions with relatively less computational effort. This also allows an automatic evolution of such systems. We describe how to evolve a neural network control system for a mobile robot using a simulator applying concepts of multi-objective optimisation. Sometimes a single objective may not be adequate to describe the desired performance of the robot. In such cases, typecasting the problem as a multi-objective problem becomes necessary. We investigate the possibility of using evolutionary algorithms to evolve a controller for a mobile robot with multiple objectives to be satisfied simultaneously.  Such behavior includes obstacle avoidance, smooth motion and target acquisition. The novelty of this method lies in the evolution of different navigational behaviours simultaneously using concepts of Pareto-optimality and evolutionary algorithms. A neural network is used to provide the control structure for the navigation of the mobile robot. A multi-objective evolutionary algorithm (FSGA) is used to identify the optimal neural network weights. The simulation results show that the proposed methodology is efficient and robust for evolving different behaviours simultaneously. Simulation results are provided and discussed},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(  Uran:gecco05lbp,
  author = {Bora Uran and Michael Gargano},
  title = {Data Mining Using Hybrid Evolutionary Models for Creating Data Classification Rules},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/76-uran.pdf},
  keywords = {classification rules, hybrid evolutionary models, life cycle model},
  abstract = {Classification rules reflect information that can be extracted from a database using data mining.  We began by considering a hybrid  (i.e., particle swarm, genetic algorithm, hill climber) model to evolve the rules. We study hybrid heuristic models in the context of classification rule discovery. Nature inspired search algorithms such as Genetic Algorithms, Ant Colonies and Particle Swarm Optimization have been previously applied to data mining tasks, in particular, classification rule discovery. We extend this work by applying hybrid models that combine GA, PSO and/or hill climbers to the same type of classification tasks. Such models have already been tested and proved to be better than individual standalone search algorithms in various combinatorial optimisation problems. Our research focused on investigating the same kind of potential performance enhancements in classification rule discovery tasks.  We developed a model for a hybrid heuristic based classifier and implemented different variations of it in Java. These algorithms have been benchmarked against the well-known decision tree induction algorithm C4.5 using previously studied data sets in the literature. Results have been compared in terms of prediction accuracy, speed and comprehensibility. Our results showed that, heuristic based classifiers compete with C4.5 in terms of prediction accuracy on certain data sets and outperform C4.5 in general in terms of comprehensibility. C4.5 always outperformed heuristic based classifiers in terms of speed due to the relative inefficiency inherent in heuristic based classification models. We also showed that hybridisation of heuristics could bring improvements in terms of execution speed in comparison to plain standalone heuristic based classifiers.},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Kazadi:gecco05lbp,
  author = {Sanza Kazadi and Michele Lee and Lauren Lee},
  title = {A Case for Exhaustive Optimization},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/77-kazadi.pdf},
  abstract = {Evolutionary algorithms have enjoyed a great success in a variety
of different fields ranging from numerical optimisation to general creative
design.  However, to date, the question of why this success is possible has
never been adequately determined.  We examine two algorithms,
a genetic algorithm and a pseudo-exhaustive search algorithm dubbed Directed
Exhaustive Search.  We examine the GA\'s apparent ability to compound individual
mutations, and its role in the GA\'s optimisation.  We then explore the use
of the DES algorithm using a suitably altered mutation operator mimicking the
GA\'s surreptitious compounding of the mutation operator.  We find that the
DES algorithm is capable of performing comparably to or outperforming the GA 
over all test problems, as predicted by theory},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Rababaah:gecco05lbp,
  author = {Haroun Rababaah and Dana Vrajitoru and James Wolfer},
  title = {Asphalt pavement crack classification: A comparison of GA, MLP, and SOM},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/78-rababaah.pdf},
  abstract = {Asphalt pavement distress, the various defects such as holes and cracks, represent a significant engineering and economic concern. It is estimated that pavement defects cause damage costing $10 billion each year in the United States alone [10].  One important step in managing  this problem is accurately assessing the pavement condition and its change over time.  We compare three methods for automatically classifying pavement cracks, genetic algorithms, multilayer perceptrons, and self-organizing maps.  We also discuss the impact of feature representation on the resulting classification.  Our best classifiers demonstrated accuracies between 86 and 98%},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Silva:gecco05lbp,
  author = {Sara Silva and Yao-Ting Tseng},
  title = {Classification of Seafloor Habitats using Genetic Programming},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/79-silva.pdf},
  keywords = {genetic algorithms, genetic programming},
  abstract = {We use genetic programming for the classification of different seafloor habitats, based on the acoustic backscatter data from an echo sounder. By developing a different fitness function and dividing the multiple-class problem into several two-class problems, we were able to improve the results presented in a previously published work, providing a better discrimination between most of the seafloor types used in this study. We discuss the quality of these results and provide ideas to further improve the classification performance},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Yong:gecco05lbp,
  author = {Chern Han Yong and Kenneth O. Stanley and Risto Mikkulainen},
  title = {Incorporating Advice into Evolution of Neural Networks},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/80-yong.pdf},
  abstract = {Neuroevolution is a promising learning method in tasks with
  extremely large state and action spaces and hidden states.  Recent
  advances allow neuroevolution to take place in real time, making it
  possible to e.g. construct video games with adaptive agents.  Often
  some of the desired behaviours for such agents are known, and it
  would make sense to prescribe them, rather than requiring evolution
  to discover them. We present a technique for incorporating
  human-generated advice in real time into neuroevolution. The advice
  is given in a formal language and converted to a neural network
  structure through KBANN. The NEAT neuroevolution method then
  incorporates the structure into existing networks through evolution
  of network weights and topology. The method is evaluated in the NERO
  video game, where it makes learning faster even when the tasks
  change and novel ways of making use of the advice are required. Such
  ability to incorporate human knowledge into neuroevolution in real
  time may prove useful in several interactive adaptive domains in the
  future},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Khalifa:gecco05lbp,
  author = {Yaser M.A. Khalifa and Yu Jen Fan},
  title = {An FPGA-Based General Purpose Neural Network Chip With On-Chip Learning },
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/81-khalifa.pdf},
  abstract = {a description of a general purpose neural network chip with on-chip learning is given. The design is implemented using Xilinx Vertex II XCV 1000 Field Programmable Gate Array (FPGA). An XOR gate simulation was used as a testing application. Results and comparison of both software and hardware implementations are listed. A second testing application in noise cancellation and voice recognition is currently under development},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Bongard:gecco05lbp,
  author = {Josh Bongard and Hod Lipson},
  title = {Reinventing the Wheel: An Experiment in Geometric Innovation},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/82-bongard.pdf},
  abstract = {In the domain of design, there are two ways of viewing the competitiveness of evolved structures: they either improve in some manner on previous solutions; they produce alternative designs that were not previously considered; or they achieve both. We show that the way in which designs are genetically encoded influences which alternative structures are discovered, for problems in which a set of more than one optimal solution exists. The problem considered is one of the most ancient known to humanity: design a two-dimensional shape that, when rolled across flat ground, maintains a constant height. It was not until the late 19th century---roughly 7000 years after the discovery of the wheel---that Franz Reuleaux showed that a circle is not the only optimal solution. Here we demonstrate that artificial evolution repeats this discovery in under one hour},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(White:gecco05lbp,
  author = {Bill White and Joshua Gilbert and Jason Moore},
  title = {A Statistical Comparison of Grammatical Evolution Strategies in the Domain of Human Genetics},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/83-white.pdf},
  abstract = {Detecting and characterising genetic predictors of human disease susceptibility is an important goal in human genetics. New chip-based technologies are available that facilitate
the measurement of thousands of DNA sequence variations across the human genome. Biologically-inspired stochastic search algorithms are expected to play an important role in the analysis of these high-dimensional datasets. We simulated datasets with up to 6000 attributes using two different genetic models and  statistically compared the performance of grammatical evolution, grammatical swarm, and
random search for building symbolic discriminant functions. We found no statistical difference among search algorithms within this specific domain},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Anand:gecco05lbp,
  author = {Vic Anand and Hod Lipson and Francisco Valero-Cuevas},
  title = {Blind Inference of Nonlinear Cable Network Topology from Sparse Data},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/84-anand.pdf},
  abstract = {We demonstrate a method to infer the topology of a hidden nonlinear network of cable-like links (elements that can sustain only tensile loads) given limited data on the hidden network. The hidden networks are based on two classical representations of the tendon network of a human finger, containing 11 and 16 links, respectively. The phenotype consists of a regular, mesh-like network of 72 links whose properties are modified by our evolutionary algorithm. Using 30 data points, randomly obtained from the hidden network, we evolved networks whose functional behaviour reproduced that of the hidden network, and whose topology closely matched that of the hidden network},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Li:gecco05lbp,
  author = {Xin Li and Chi Zhou and Weimin Xiao and Peter C. Nelson},
  title = {Prefix Gene Expression Programming},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/85-li.pdf},
  keywords = {genetic algorithms, genetic programming, gene expression programming},
  abstract = {Gene Expression Programming (GEP) is a powerful evolutionary method derived from Genetic Programming (GP) for model learning and knowledge discovery. However, when dealing with complex problems, its genotype under Karva notation does not allow hierarchical composition of the solution, which impairs the efficiency of the algorithm. We propose a new representation scheme based on prefix notation that overcomes the original GEP’s drawbacks. The resulted algorithm is called Prefix GEP (P-GEP). The major advantages with P-GEP include the natural hierarchy in forming the solutions and more protective genetic operations for substructure components. An artificial symbolic regression problem and a set of benchmark classification problems from UCI machine learning repository have been tested to demonstrate the applicability of P-GEP. The results show that P-GEP follows a faster fitness convergence curve and the rules generated from P-GEP consistently achieve better average classification accuracy compared with GEP},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Jin:gecco05lbp,
  author = {Yaochu Jin and Markus Olhofer and Bernhard Sendhoff},
  title = {Finding the Optimal Search Dimension for Evoultion Strategies with a small Population},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/86-jin.pdf},
  abstract = {Small populations are very desirable for reducing the required computational resources in evolutionary optimisation of complex real-world problems. Unfortunately, the search performance of small populations often reduces dramatically in a large search space. To address this problem, a method to find an optimal search dimension for small populations  is suggested. The basic idea is that the evolutionary algorithm starts with a small search dimension and then the search dimension is increased during the optimization. The search dimension will continue to increase if an increase in the search dimension improves the search performance. Otherwise, the search dimension will be decreased and then kept constant. Through empirical studies on a test problem with an infinite search dimension, we show that the proposed algorithm is able to find the search dimension that is the most efficient for the given population size.},
  notes = {Distributed on CD-ROM at GECCO-2005},
)

@InProceedings(Reed:gecco05lbp,
  author = {Martin Reed and Stefan Schenk and Gunter Swoboda},
  title = {FTO: A genetic algorithm for tunnel design optimisation},
  booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}},
  year = {2005},
  month = {25-29 June},
  editor = {Franz Rothlauf},
  address = {Washington, D.C., USA},
  url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/87-reed.pdf},
  abstract = {We present a genetic algorithm for automating the design process for underground tunnels. The tunnel cross-sectional profile must enclose a prescribed minimum space polygon, and for stability the bending moments in the shotcrete lining must not exceed certain limits. The engineer seeks to achieve these aims while minimising the volume of rock to be excavated. We describe the five parameters defining the tunnel profile, and develop a fitness function to model these design objectives. The genetic algorithm package, FTO, includes 2D finite element mesh generation and analysis modules, and sophisticated graphics using Microsoft .NET technology. A realistic example is solved in 30 generations, and the result compared with engineering experience},
  notes = {Distributed on CD-ROM at GECCO-2005},
)