Programming Perl
eaLib - A Java Framework for Implementation of Evolutionary Algorithms
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
JCLEC: a Java framework for evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (pp 315-357) "Ordered structures in many-valued logic"
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Learning perl
A Perl primer for evolutionary algorithm practitioners
ACM SIGEVOlution
SofEA: a pool-based framework for evolutionary algorithms using CouchDB
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Improving evolutionary solutions to the game of mastermind using an entropy-based scoring method
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Designing and testing a pool-based evolutionary algorithm
Natural Computing: an international journal
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While a lot of attention is usually devoted to the study of different components of evolutionary algorithms or the creation of heuristic operators, little effort is being directed at how these algorithms are actually implemented. However, the efficient implementation of any application is essential to obtain a good performance, to the point that performance improvements obtained by changes in implementation are usually much bigger than those obtained by algorithmic changes, and they also scale much better. In this paper we will present and apply usual methodologies for performance improvement to evolutionary algorithms, and show which implementation options yield the best results for a certain problem configuration and which ones scale better when features such as population or chromosome size increase.