Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Religion-Based Spatial Model for Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Advanced models of cellular genetic algorithms evaluated on SAT
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Cheating for problem solving: a genetic algorithm with social interactions
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
The spatially-dispersed genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Multiobjective evolutionary computation for supersonic wing-shapeoptimization
IEEE Transactions on Evolutionary Computation
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The Genetic Algorithm (GA) has been one of the most studied topics in evolutionary algorithm literature. Mimicking natural processes of inheritance, mutation, natural selection and genetic operators, GAs have been successful in solving various optimization problems. However, standard GA is often criticized as being too biased in candidate solutions due to genetic drift in search. As a result, GAs sometimes converge on premature solutions. In this paper, we survey the major advances in GA, particularly in relation to the class of structured population GAs, where better exploration and exploitation of the search space is accomplished by controlling interactions among individuals in the population pool. They can be classified as spatial segregation, spatial distance and heterogeneous population. Additionally, secondary factors such as aging, social behaviour, and so forth further guide and shape the reproduction process. Restricting randomness in reproduction has been seen to have positive effects on GAs. It is our hope that by reviewing the many existing algorithms, we shall see even better algorithms being developed.