CEGO: C++11 Evolutionary Global Optimization

Co-Evolutionary Global Optimization Algorithm

A hybrid global optimization method, the coevolutionary global optimization algorithm, is proposed which utilizes the self-organized critical state as the mean of diversification of search and the traditional conjugate gradient local minimization method as the mean of intensification of search. The former has been recently used by Boettcher and Percus (Artificial Intelligence 119 (2000) 275) to...

Evaluating uego, an evolutionary global optimization algorithm

Evolutionary annealing: global optimization in measure spaces

Stochastic optimization methods such as evolutionary algorithms and Markov Chain Monte Carlo methods usually involve a Markov search of the optimization domain. Evolutionary annealing is an evolutionary algorithm that leverages all the information gathered by previous queries to the cost function. Evolutionary annealing can be viewed either as simulated annealing with improved sampling or as a ...

Global optimization via evolutionary search with soft selection

The aim of this work is to draw a comparison of four variants of the Evolutionary Search with Soft Selection (ESSS) algorithms based on selected parameter optimization problems. They are tested with nine objective functions, most of them being strongly non-linear and multimodal. From the results obtained it follows that all modified ESSS algorithms are more effective and generally faster than t...

A fast annealing evolutionary algorithm for global optimization

By combining the aspect of population in genetic algorithms (GAs) and the simulated annealing algorithm (SAA), a novel algorithm, called fast annealing evolutionary algorithm (FAEA), is proposed. The algorithm is similar to the annealing evolutionary algorithm (AEA), and a very fast annealing technique is adopted for the annealing procedure. By an application of the algorithm to the optimizatio...