Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

Nonlinear constrained programing problem (NCPP) has been arisen in diverse range of sciences such as portfolio, economic management etc.. In this paper, a multiobjective imperialist competitive evolutionary algorithm for solving NCPP is proposed. Firstly, we transform the NCPP into a biobjective optimization problem. Secondly, in order to improve the diversity of evolution country swarm, and he...

Optimization of turning process is a non-linear optimization with constrains and it is difficult for the conventional optimization algorithms to solve this problem. The purpose of present study is to demonstrate the potential of Imperialist Competitive Algorithm (ICA) for optimization of multipass turning process. This algorithm is inspired by competition mechanism among imperialists and coloni...

this paper presents a biogeography-based optimization (bbo) algorithm to solve the economic loaddispatch (eld) problem with generator constraints in thermal plants. the applied method can solvethe eld problem with constraints like transmission losses, ramp rate limits, and prohibited operatingzones. biogeography is the science of the geographical distribution of biological species. the modelsof...

| 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 problem while solving the problem. In this paper we develop a classiication of adaptation on the basis of the mechanisms used, and the level at which adaptation operates within the evolutionary algorithm. The classiication covers all fo...

|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...

In this paper we present an efficient decomposition technique to speed up evolutionary algorithms when dealing with large scale optimization problems. Divide and conquer methods aim to solving problems in smaller entities and then combining the sub-solutions to form complete solutions. Often the optimal way to divide the problem varies as the evolutionary algorithm proceeds, thus making a stati...

An evolutionary programming algorithm with adaptivemutation operators based on L evy prob ability distribution is studied L evy stable distri bution has an in nite second moment Because of this L evy mutation is more likely to generate an o spring that is farther away from its parent than Gaussian mutation which is often used in evolu tionary algorithms Such likelihood depends on a parameter in...

Parameter control is still one of the main challenges in evolutionary computation. This paper is concerned with controlling selection operators on-the-fly. We perform an experimental comparison of such methods on three groups of test functions and conclude that varying selection pressure during a GA run often yields performance benefits, and therefore is a recommended option for designers and u...

One application of evolutionary computation is developing agents which play in team-based games. In such games, it is often beneficial for each agent in a team to specialise in a different role within the team. We present several methods of evolving such specialisation by assessing agents as members of teams, and compare their performance in evolving solutions to a simulation of an immune syste...