Stochastic local search in continuous domains: questions to be answered when designing a novel algorithm

Several population-based methods (with origins in the world of evolutionary strategies and estimation-of-distribution algorithms) for black-box optimization in continuous domains are surveyed in this article. The similarities and differences among them are emphasized and it is shown that they all can be described in a common framework of stochastic local search--a class of methods previously defined mainly for combinatorial problems. Based on the lessons learned from the surveyed algorithms, a set of algorithm features (or, questions to be answered) is extracted. An algorithm designer can take advantage of these features and by deciding on each of them, she can construct a novel algorithm. A few examples in this direction are shown. Categories and Subject Descriptors

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Petr Posik
• Keywords: Black-Box Optimization, Estimation-of-distribution Algorithms, GECCO 2010, Optimization, Several Population-based Methods