Alexandru-Adrian Tantar

We propose a bicriterion parallel hybrid genetic algorithm (GA) in order to efficiently deal with the protein structure prediction problem using the computational grid. Expand

A hierarchical hybrid model of parallel metaheuristics is proposed, combining an evolutionary algorithm and an adaptive simulated annealing. Expand

We propose a component-oriented classification for dynamic multi-objective optimization problems and introduce an online dynamic MNK-landscape formulation. Expand

The aim of this book is to provide a strong theoretical support for understanding and analyzing the behavior of evolutionary algorithms, as well as for creating a bridge between probability, set-oriented numerics and evolutionary computation. Expand

A comparative study of parallel metaheuristics executed in grid environments is proposed, having as case study a genetic algorithm, a simulated annealing algorithm and a random search method. Expand

Numerical and computational methods are nowadays used in a wide range of contexts in complex systems research, biology, physics, and engineering. Expand

A classification of dynamic multi-objective optimization problems is proposed in this article. Expand

An agent-based dynamic multi-objective evolutionary algorithm relying on simulation and anticipation mechanisms is presented in this chapter, and addresses several difficult issues in the energyefficient dynamic and autonomous management of distributed computing resources. Expand

This article outlines the recent developments in the field of large-scale parallel computing applied to molecular simulations, also including some original, preliminary contributions of the authors. Expand

A hybrid hierarchical conformational sampling evolutionary algorithm is presented in this chapter, relying on different parallelization models, enclosing conjugate gradient and adaptive simulated annealing. Expand