Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA)
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
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
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
A Diploid Genetic Algorithm for Preserving Population Diversity - pseudo-Meiosis GA
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Experimental Study of Multipopulation Parallel Genetic Programming
Proceedings of the European Conference on Genetic Programming
Genetic Operators in a Dual Genetic Algorithm
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
PDGA: the primal-dual genetic algorithm
Design and application of hybrid intelligent systems
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Distance measures based on the edit distance for permutation-type representations
Journal of Heuristics
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Adjusting population distance for the dual-population genetic algorithm
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
Where Are the Niches? Dynamic Fitness Sharing
IEEE Transactions on Evolutionary Computation
MuGA: multiset genetic algorithm
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Information Sciences: an International Journal
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
Hi-index | 0.00 |
A variety of previous works exist on maintaining population diversity of genetic algorithms (GAs). Dual-population GA (DPGA) is a type of multipopulation GA (MPGA) that uses an additional population as a reservoir of diversity. The main population is similar to that of an ordinary GA and evolves to find good solutions. The reserve population evolves to maintain and provide diversity to the main population. While most MPGAs use migration as a means of information exchange between different populations, DPGA uses crossbreeding because the two populations have entirely different fitness functions. The reserve population cannot provide useful diversity to the main population unless the two maintain an appropriate distance. Therefore, DPGA adjusts the distance dynamically to achieve an appropriate balance between exploration and exploitation. The experimental results on various classes of problems using binary, real-valued, and order-based representations show that DPGA quite often outperforms not only the standard GAs but also other GAs having additional mechanisms of diversity preservation.