- author = "Ernesto Sanchez and Massimiliano Schillaci and Giovanni Squillero",
- title = "Evolutionary Optimization: the {uGP} toolkit",
- publisher = "Springer",
- year = "2011",
- keywords = "genetic algorithms, genetic programming, Evolutionary Optimisation, Graph-based representation, Industrial Problems, MicroGP, Problem Complexity, Real-world Applications, Turing-complete, evolutionary algorithm, evolutionary computation",
- isbn13 = "978-0-387-09425-0",
-
URL = "
http://www.springer.com/computer/ai/book/978-0-387-09425-0",
-
DOI = "
DOI:10.1007/978-0-387-09426-7",
- size = "178 pages",
-
abstract = "This book describes an award-winning evolutionary
algorithm that outperformed experts and conventional
heuristics in solving several industrial problems. It
presents a discussion of the theoretical and practical
aspects that enabled uGP (MicroGP) to autonomously find
the optimal solution of hard problems, handling highly
structured data, such as full-fledged assembly
programs, with functions and interrupt handlers.
For a practitioner, microGP is simply a versatile optimiser to tackle most problems with limited setup effort. The book is valuable for all who require heuristic problem-solving methodologies, such as engineers dealing with verification and test of electronic circuits; or researchers working in robotics and mobile communication. Examples are provided to guide the reader through the process, from problem definition to gathering results.
For an evolutionary computation researcher, muGP may be regarded as a platform where new operators and strategies can be easily tested.
MicroGP (the toolkit) is an active project hosted by Sourceforge: http://ugp3.sourceforge.net/",
- notes = "Evolutionary Optimization: the GP toolkit https://extras.springer.com/?query=978-0-387-09425-0",
