Abstract
This paper proposes a computational prototype for automatic design of optimization algorithms. The proposed scheme makes an analysis of the problem that estimates the degree of separability of the optimization problem. The separability is estimated by computing the Pearson correlation indices between pairs of variables. These indices are then manipulated to generate a unique index that estimates the separability of the entire problem. The separability analysis is thus used to design the optimization algorithm that addresses the needs of the problem. This prototype makes use of two operators arranged in a Parallel Memetic Structure. The first operator performs moves along the axes while the second simultaneously perturbs all the variables to follow the gradient of the fitness landscape. The resulting algorithmic implementation, namely Separability Prototype for Automatic Memes (SPAM), has been tested on multiple testbeds and various dimensionality levels. The proposed computational prototype proved to be a flexible and intelligent framework capable to learn from a problem and, thanks to this learning, to outperform modern meta-heuristics representing the-state-of-the-art in optimization. (C) 2014 Elsevier Inc. All rights reserved.