—Over the last two decades, many Differential Evolution (DE) strategies have been introduced for solving Optimization Problems. Due to the variability of the characteristics in o...
Abstract—In this paper, we propose a framework for employing opposition-based learning to assist evolutionary algorithms in solving discrete and combinatorial optimization proble...
—Evolutionary algorithms have been very popular optimization methods for a wide variety of applications. However, in spite of their advantages, their computational cost is still ...
Mohsen Davarynejad, Jafar Rezaei, Jos L. M. Vranck...
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
—We develop a stochastic local search algorithm for finding Pareto points for multi-criteria optimization problems. The algorithm alternates between different single-criterium o...
—In many systems providing storage and retrieval operations on data, indices are used to make these operations more efficient. Distributed storage systems provide means to distr...
—Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as GO, where the level of play...
Abstract—Higher organisms are able to respond to continuously changing external conditions by transducing cellular signals into specific regulatory programs, which control gene ...
— The aim of this paper is to introduce the use of Tower Defence (TD) games in Computational Intelligence (CI) research. We show how TD games can provide an important test-bed fo...
Phillipa Avery, Julian Togelius, Elvis Alistar, Ro...