Gradual distributed real-coded genetic algorithms

By Francisco Herrera, Manuel Lozano. IEEE Transactions on Evolutionary Computation, 2000

309 citations recorded by the source corpus.

A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the so-railed heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed real-coded genetic algorithms, a type of heterogeneous distributed real-coded genetic algorithms that apply a different crossover operator to each sub-population. Experimental results show that the proposals consistently outperform sequential real-coded genetic algorithms.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b622019c7f7a4fa907461a965bb3d6d217761fb4

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Citations in the corpus

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art (2015, background): “coarse-grained model) [56,99], and cellular (a.”
Parallelism and evolutionary algorithms (2002, methodology): “Some other coarse-grain models like CoPDEB [2] and GDGA [65] have been designed for specific goals, such as providing explicit exploration/exploitation by applying different operators on each island.”
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms (2005, background): “Among them, it is worth making a special mention of heterogeneous EAs [19], [20], in which algorithms with different features run in multiple subpopulations and collaborate in order to avoid premature convergence.”
Real-Coded Memetic Algorithms with Crossover Hill-Climbing (2004, methodology): “CHC has arisen as a reference point in the GA literature (Herrera and Lozano, 2000b; Whitley, Rana, Dzubera and Mathias, 1996).”
Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems (2011, methodology): “This problem has been tackled using Genetic Algorithms (GAs) in [1], [2].”
Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (2002, background): “Different fuzzified versions of genetic operators have been proposed, such as fuzzy connective-based crossover (Herrera, Lozano, and Verdegay, 1996, 1997; Herrera and Lozano, 2000) and soft genetic operators (Voigt, Miihlenbein, and Cvetkovic, 1995).”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, background): “Owing to the great difficulty of solving this problem with high accuracy without specific operators for continuous optimization (like gradual GAs [52]), we stop the algorithm when the number of function evaluations exceeds 10 5 .”