Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Genetic programming using a minimum description length principle
Advances in genetic programming
A parallel genetic algorithm for the set partitioning problem
A parallel genetic algorithm for the set partitioning problem
A Theory of Program Size Formally Identical to Information Theory
Journal of the ACM (JACM)
Robot Motion Planning
Genetic Algorithms and Robotics
Genetic Algorithms and Robotics
Clustering Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms to Schedule Flow Shop Releases
Proceedings of the 3rd International Conference on Genetic Algorithms
GADELO: A Multi-Population Genetic Algorithm Based on Dynamic Exploration of Local Optima
Proceedings of the 5th International Conference on Genetic Algorithms
On Solving Travelling Salesman Problems by Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Complexity of the mover's problem and generalizations
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
The crowding approach to niching in genetic algorithms
Evolutionary Computation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Bi-objective multipopulation genetic algorithm for multimodal function optimization
IEEE Transactions on Evolutionary Computation
Multivariate multi-model approach for globally multimodal problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation
Computers and Electrical Engineering
mDBN: motif based learning of gene regulatory networks using dynamic bayesian networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Computers and Operations Research
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A novel genetic algorithm (GA) using minimal representation size cluster (MRSC) analysis is designed and implemented for solving multimodal function optimization problems. The problem of multimodal function optimization is framed within a hypothesize-and-test paradigm using minimal representation size (minimal complexity) for species formation and a GA. A multiple-population GA is developed to identify different species. The number of populations, thus the number of different species, is determined by the minimal representation size criterion. Therefore, the proposed algorithm reveals the unknown structure of the multimodal function when a priori knowledge about the function is unknown. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The proposed scheme results in a highly parallel algorithm for finding multiple local minima. In this paper, a path-planning algorithm is also developed based on the MRSC-GA algorithm. The algorithm utilizes MRSC_GA for planning paths for mobile robots, piano-mover problems, and N-link manipulators. The MRSC_GA is used for generating multipaths to provide alternative solutions to the path-planning problem. The generation of alternative solutions is especially important for planning paths in dynamic environments. A novel iterative multiresolution path representation is used as a basis for the GA coding. The effectiveness of the algorithm is demonstrated on a number of two-dimensional path-planning problems.