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GECCO
2007
Springer

An informed convergence accelerator for evolutionary multiobjective optimiser

9 years 4 months ago
An informed convergence accelerator for evolutionary multiobjective optimiser
A novel optimisation accelerator deploying neural network predictions and objective space direct manipulation strategies is presented. The concept of directing the search through the use of ‘mirage’ solutions is introduced and investigated. The accelerator is meant to be a portable component that can be plugged into any stochastic optimisation algorithm, such as genetic algorithms. The purpose of the new component termed as the Informed Convergence Accelerator (ICA) is to enhance the search capability, convergence extent and most especially the speed of convergence of the hosting stochastic global optimisation technique. ICA was hybridized with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Enhanced results were achieved demonstrating the utility of the introduced component. Categories and Subject Descriptors I.2.8 Computing Methodologies: Artificial Intelligence -Problem Solving, Control Methods and Search. General Terms Algorithms, Performance, Experimentation, Keywords ...
Salem F. Adra, Ian Griffin, Peter J. Fleming
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Salem F. Adra, Ian Griffin, Peter J. Fleming
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