A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Self-adaptation in evolving systems
Artificial Life
niGAVaPS — outbreeding in genetic algorithms
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Using assortative mating in genetic algorithms for vector quantization problems
Proceedings of the 2001 ACM symposium on Applied computing
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Enhancing GA Performance through Crossover Prohibitions Based on Ancestry
Proceedings of the 6th International Conference on Genetic Algorithms
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Error thresholds in genetic algorithms
Evolutionary Computation
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
Agent-Based Model of Genotype Editing
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
A self-organized criticality mutation operator for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Dual-population genetic algorithm for nonstationary optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Self-adjusting the intensity of assortative mating in genetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolvable Agents in Static and Dynamic Optimization Problems
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Information Sciences: an International Journal
Tracking Extrema in Dynamic Fitness Functions with Dissortative Mating Genetic Algorithms
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
Genetic Algorithms with Elitism-Based Immigrants for Changing Optimization Problems
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A dual-population genetic algorithm for adaptive diversity control
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Self-regulated population size in evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Assortative mating in genetic algorithms for dynamic problems
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A novel bio-inspired approach based on the behavior of mosquitoes
Information Sciences: an International Journal
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Traditional Genetic Algorithms (GAs) mating schemes select individuals for crossover independently of their genotypic or phenotypic similarities. In Nature, this behavior is known as random mating. However, non-random protocols, in which individuals mate according to their kinship or likeness, are more common in natural species. Previous studies indicate that when applied to GAs, dissortative mating - a type of non-random mating in which individuals are chosen according to their similarities - may improve their performance (on both speed and reliability). Dissortative mating maintains genetic diversity at a higher level during the run, a fact that is frequently observed as a possible cause of dissortative GAs' ability to escape local optima. Dynamic optimization demands a special attention when designing and tuning a GA, since diversity plays an even more crucial role than it does when tackling static ones. This paper investigates the behavior of the Adaptive Dissortative Mating GA (ADMGA) in dynamic problems and compares it to GAs based on random immigrants. ADMGA selects parents according to their Hamming distance, via a self-adjustable threshold value. The method, by keeping population diversity during the run, provides an effective means to deal with dynamic problems. Tests conducted with dynamic trap functions and dynamic versions of Road Royal and knapsack problems indicate that ADMGA is able to outperform other GAs on a wide range of tests, being particularly effective when the frequency of changes is low. Specifically, ADMGA outperforms two state-of-the-art algorithms on many dynamic scenarios. In addition, and unlike preceding dissortative mating GAs and other evolutionary techniques for dynamic optimization, ADMGA self-regulates the intensity of the mating restrictions and does not increase the set of parameters in GAs, thus being easier to tune.