Evolutionary Computation for Expensive Optimization: A Survey

Evolutionary Optimization for Computationally expensive problems using Gaussian Processes

The use of statistical models to approximate detailed analysis codes for evolutionary optimization has attracted some attention [1-3]. However, those early methodologies do suffer from some limitations, the most serious of which being the extra tuning parameter introduceds. Also the question of when to include more data points to the approximation model during the search remains unresolved. Tho...

Hybrid Evolutionary Computation for Continuous Optimization

Hybrid optimization algorithms have gained popularity as it has become apparent there cannot be a universal optimization strategy which is globally more beneficial than any other. Despite their popularity, hybridization frameworks require more detailed categorization regarding: the nature of the problem domain, the constituent algorithms, the coupling schema and the intended area of application...

Evolutionary computation for modeling and optimization

A Survey of Practitioners of Evolutionary Computation

To assist in understanding trends in the field of Evolutionary Computation (EC) and in helping graduates find jobs in EC we conducted a survey from March 2005 to February 2006 on members of the EC community. The analysis reveals various technology transfer strategies and activities took place during the past 50 years: parallel exploration of multiple application areas; a combination of exploita...

Adaptation in Evolutionary Computation: A Survey

|Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation; it tunes the algorithm to the problemwhile solving the problem. In this paper we develop a classi cation of adaptation on the basis of the mechanisms used, and the level at which adaptation operates within the evolutionary algorithm. The classi cation covers all form...