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Continuous optimisation theory made easy? Finite-element models of evolutionary strategies, genetic algorithms and particle swarm optimizers (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Poli, R. Langdon, W. B. Clerc, M. Stephens, C. R. |
| Abstract | Abstract. We propose a method to build discrete Markov chain models of continuous stochastic optimisers that can approximate them on arbitrary continuous problems to any precision. We discretise the objective function using a finite element method grid which produces corresponding distinct states in the search algorithm. Iterating the transition matrix gives precise information about the behaviour of the optimiser at each generation, including the probability of it finding the global optima or being deceived. The approach is tested on a (1+1)-ES, a bare bones PSO and a real-valued GA. The predictions are remarkably accurate. 1 |
| File Format | |
| Language | English |
| Publisher Date | 2007-01-01 |
| Publisher Department | Department of Computer Science, University of Essex |
| Access Restriction | Open |
| Subject Keyword | Continuous Optimisation Theory Particle Swarm Optimizers Genetic Algorithm Finite-element Model Evolutionary Strategy Transition Matrix Arbitrary Continuous Problem Search Algorithm Real-valued Ga Global Optimum Finite Element Method Grid Precise Information Continuous Stochastic Optimisers Discrete Markov Chain Model Distinct State Objective Function |
| Content Type | Text |
| Resource Type | Technical Report |
