Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation

Guillen, A.; Rojas, I.; Gonzalez, J.; Pomares, H.; Herrera, L. J.; Paechter, B.

Abstract

Nature shows many examples where the specialisation of elements aimed to solve different problems is successful. There are explorer ants, worker bees, etc., where a group of individuals is assigned a specific task. This paper will extrapolate this philosophy, applying it to a multiobjective genetic algorithm. The problem to be solved is the design of Radial Basis Function Neural Networks (RBFNNs) that approximate a function. A non distributed multiobjective algorithm will be compared against a parallel approach that emerges as a straight forward specialisation of the crossover and mutation operators in different islands. The experiments will show how, as in the real world, if the different islands evolve specific aspects of the RBFNNs, the results are improved.

Citation

Guillen, A., Rojas, I., Gonzalez, J., Pomares, H., Herrera, L. J., & Paechter, B. (2006, December). Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation. Presented at 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 19th Australian Joint Conference on Artificial Intelligence
• Start Date: Dec 4, 2006
• End Date: Dec 8, 2006
• Publication Date: 2006
• Deposit Date: Oct 9, 2019
• Publisher: Springer
• Pages: 1127-1132
• Series Title: Lecture Notes in Computer Science
• Series Number: 4304
• Series ISSN: 0302-9743
• Book Title: AI 2006: Advances in Artificial Intelligence
• DOI: https://doi.org/10.1007/11941439_135
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/2672