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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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IEEE Computational Intelligence Magazine
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Information Sciences: an International Journal
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Evolutionary Computation
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Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Compact Particle Swarm Optimization
Information Sciences: an International Journal
A hybrid compact genetic algorithm applied to the multi-level capacitated lot sizing problem
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Improving the performance of the BioHEL learning classifier system
Expert Systems with Applications: An International Journal
Securing high resolution grayscale facial captures using a blockwise coevolutionary GA
Expert Systems with Applications: An International Journal
A hybrid cGA applied to the MLCLSP with overtime
ACM SIGAPP Applied Computing Review
Information Sciences: an International Journal
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Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of the GA's parameters and operators. The paper clearly illustrates the mapping of the simple GA's parameters into those of an equivalent compact GA. Computer simulations compare both algorithms in terms of solution quality and speed. Finally, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GAs