Solving CSPs with evolutionary algorithms using self-adaptive constraint weights.

Eiben, A E; Jansen, B; Michalewicz, Z; Paechter, Ben

Abstract

This paper examines evolutionary algorithms (EAs) extended by various penalty-based approaches to solve constraint satisfaction
problems (CSPs). In some approaches, the penalties are set in advance and they do not change during a run. In other approaches,
dynamic or adaptive penalties that change during a run according to some mechanism (a heuristic rule or a feedback), are used. In
this work we experimented with self-adaptive approach, where the penalties change during the execution of the algorithm, however, no
feedback mechanism is used. The penalties are incorporated in the individuals and evolve together with the solutions.

Citation

Eiben, A. E., Jansen, B., Michalewicz, Z., & Paechter, B. (2000, July). Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. Presented at Genetic and Evolutionary Computation Conference (GECCO-2000)

• Conference Name: Genetic and Evolutionary Computation Conference (GECCO-2000)
• Start Date: Jul 8, 2000
• End Date: Jul 12, 2000
• Publication Date: 2000
• Deposit Date: Aug 2, 2010
• Publicly Available Date: Aug 2, 2010
• Peer Reviewed: Peer Reviewed
• Pages: 128-134
• Book Title: GECCO-2000 : proceedings of the genetic and evolutionary computation conference
• ISBN: 1558607080
• Keywords: evolutionary algorithms; constraint satisfactionproblems (CSPs); self-adaptive
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/3198
• Additional Information: A Joint Meeting of the Ninth International Conference on Genetic Algorithms (ICGA-2000) and the Fifth Annual Genetic Programming Conference (GP-2000)
• Contract Date: Aug 2, 2010