In this paper, we propose a multi-agent approach for solving a class of optimization problems involving expensive resources, where monolithic local search schemes perform miserabl...
This paper reports on the development of a novel island model for evolutionary algorithms, which is intrinsically parallel and intended to better utilise resources and outlier sol...
AbstractThis paper presents a real-coded memetic algorithm that combines a high diversity global exploration with an adaptive local search method to the most promising individuals ...
Seki is a situation of coexistence in the game of Go, where neither player can profitably capture the opponent’s stones. This paper presents a new method for deciding whether an...
We consider a possible scenario of experimental analysis on heuristics for optimization: identifying the contribution of local search components when algorithms are evaluated on th...
This work concentrates on the design of a system intended for study of advanced scheduling techniques for planning various types of jobs in a Grid environment. The solution is able...
This paper is about Reinforcement Learning (RL) applied to online parameter tuning in Stochastic Local Search (SLS) methods. In particular a novel application of RL is considered i...
We consider the problem of designing error correcting codes (ECC), a hard combinatorial optimization problem of relevance in the field of telecommunications. This problem is firs...
Jhon Edgar Amaya, Carlos Cotta, Antonio J. Fern&aa...
Ant colony optimization (ACO) is a well known metaheuristic. In the literature it has been used for tackling many optimization problems. Often, ACO is hybridized with a local sear...
Adaptive Memetic Algorithms couple an evolutionary algorithm with a number of local search heuristics for improving the evolving solutions. They are part of a broad family of meta...