Meta-heuristic algorithms are problem-solving methods which try to find good-enough
solutions to very hard optimization problems, at a reasonable computation time, where …

In this work we present an overview of the most prominent population-based algorithms and
the methodologies used to extend them to multiple objective problems. Although not exact in …

This paper introduces a software tool named KEEL which is a software tool to assess
evolutionary algorithms for Data Mining problems of various kinds including as regression …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

DEAP (Distributed Evolutionary Algorithms in Python) is a novel volutionary computation
framework for rapid prototy** and testing of ideas. Its design departs from most other …

This paper presents a modular framework for meta-heuristic optimization of complex
optimization tasks by decomposing them into subtasks that may be designed and developed …

In this paper, we present the ParadisEO white-box object-oriented framework dedicated to
the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a …

In vertebrates, skin colour patterns emerge from nonlinear dynamical microscopic systems of
cell interactions. Here we show that in ocellated lizards a quasi-hexagonal lattice of skin …

Feature selection is a necessary critical stage in data mining process. There is always an
arm race to build frameworks and libraries that ease and automate this process. In this …

Many optimization problems cannot be solved by classical mathematical optimization
techniques due to their complexity and the size of the solution space. In order to achieve …