Elsevier

Advances in Computers

Volume 45, 1997, Pages 155-196
Advances in Computers

Optimization Via Evolutionary Processes

https://doi.org/10.1016/S0065-2458(08)60708-1Get rights and content

Abstract

Evolutionary processes have attracted considerable interest in recent years for solving a variety of optimization problems. This article presents a synthesizing overview of the underlying concepts behind evolutionary algorithms, a brief review of genetic algorithms, and motivation for hybridizing genetic algorithms with other methods. Operating concepts governing evolutionary strategies and differences between such strategies and genetic algorithms are highlighted. Genetic programming techniques and their application are discussed briefly. To demonstrate the applicability of these principles, representative examples are drawn from different disciplines.

References (51)

  • A. Bertoni et al.

    Implicit parallelism in genetic algorithms

    Artificial Intelligence

    (1993)
  • W.D. Hillis

    Co-evolving parasites improve simulated evolution as an optimisation procedure

    Physica

    (1990)
  • A.K. Majhi et al.

    A genetic algorithm-based circuit partitioner for MCMs

    Microprocessing and Microprogramming, The Euromicro Journal

    (1995)
  • W. Atmar

    Notes on the simulation of evolution

    IEEE Transactions on Neural Networks

    (1994)
  • Baluja, S. (1994). Population Based Incremental Learning: A Method for Integrating Genetic Search Based Function...
  • T. Back et al.

    Extended selection mechanisms in genetic algorithms

  • M.F. Bramlette

    Initialization, mutation and selection methods in GAs for function optimization

  • J.P. Cohoon et al.

    A multipopulation genetic algorithm for solving the K-partition problem on hypercubes

  • Y. Davidor

    A naturally occurring niche and species phenomenon: the model and first results

  • M. Dorigo et al.

    Ant system: optimization by a colony of co-operating agents

    IEEE Transactions on Systems, Man and Cybernetics

    (1996)
  • D.B. Fogel

    An introduction to simulated evolutionary optimization

    IEEE Transactions on Neural Networks

    (1994)
  • D.E. Goldberg et al.

    Don't worry, be messy

  • J.J. Grefenstette

    Optimization of control parameters for GAs

    IEEE Transactions on Systems, Man and Cybernetics

    (1986)
  • T. Back et al.

    A survey of evolution strategies

  • Hoffmeister, F. & Back, T. (1992). Genetic Algorithms and Evolution Strategies: Similarities and Differences. Technical...
  • T. Kido et al.

    Analysis and comparisons of GA, SA, TABU search and evolutionary combination algorithms

    Informatica

    (1994)
  • J.R. Koza

    Genetic Programming On the Programming of Computers by Means of Natural Selection

    (1993)
  • B.F. Lisanke et al.

    Testability driven random test-pattern generator

    IEEE Transactions on CAD

    (1987)
  • N. Mansour et al.

    A hybrid genetic algorithm for task allocation in multicomputers

  • V. Nissen

    Solving the quadrature assignment problem with clues from nature

    IEEE Transactions on Neural Networks

    (1994)
  • Prahalada Rao, B.P. (1994). Evolutionary Approaches to VLSI Channel Routing. Ph.D. Dissertation, Indian Institute of...
  • W.M. Spears et al.

    On the virtues of parameterized uniform crossover

  • Srinivas, M. (1993). Genetic Algorithms: Novel Models and Fitness Based Adaptive Disruption Strategies. Ph.D....
  • Vemuri, R. (1994). Genetic Algorithms for Partitioning, Placement and Layer Assignment for Multi Chip Modules. Ph.D....
  • M.D. Vose et al.

    Schema disruption

  • Cited by (4)

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