1 Department of Computer Science, Faculty of Science, Aarhus University, Aarhus University2 EVALife Group, Department of Computer Science, University of Aarhus3 Depto. Arquitectura y Tecnología de Computadores, Escuela Técnica Superior de Ingenería Informática4 Application software, Deutsches Klimarechenzentrum5 Vorarlberg University of Applied Sciences. Department of Computer Science, PPE6 Department of Signal Theory amd Communications, Universidad Carlos III de Madrid7 Dortmund University of Technology
Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the LifeCycle model that simultaneously applies genetic algorithms (GAs), particle swarm optimisation (PSOs), and stochastic hill climbing to create a generally well-performing search heuristics. In the LifeCycle model, we consider candidate solutions and their fitness as individuals, which, based on their recent search progress, can decide to become either a GA individual, a particle of a PSO, or a single stochastic hill climber. First results from a comparison of our new approach with the single search algorithms indicate a generally good performance in numerical optimization.
Lecture Notes in Computer Science: Parallel Problem Solving From Nature --- Ppsn Vii, 2002, p. 621-630
Main Research Area:
Parallel Problem Solving from Nature --- PPSN VII, 2002