Muniglia, Mathieu, Verel, Sébastien, Pallec, Jean-Charles Le, and Do, Jean-Michel
International Conference on Artificial Evolution (Evolution Artificielle), Oct 2017, Paris, France. pp.30-46
Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Artificial Intelligence, and Computer Science - Neural and Evolutionary Computing
Abstract
In the context of the introduction of intermittent renewable energies, we propose to optimize the main variables of the control rods of a nuclear power plant to improve its capability to load-follow. The design problem is a black-box combinatorial optimization problem with expensive evaluation based on a multi-physics simulator. Therefore, we use a parallel asynchronous master-worker Evolutionary Algorithm scaling up to thousand computing units. One main issue is the tuning of the algorithm parameters. A fitness landscape analysis is conducted on this expensive real-world problem to show that it would be possible to tune the mutation parameters according to the low-cost estimation of the fitness landscape features.
Dreo, Johann, Liefooghe, Arnaud, Verel, Sébastien, Schoenauer, Marc, Merelo, Juan J., Quemy, Alexandre, Bouvier, Benjamin, and Gmys, Jan
Subjects
Computer Science - Neural and Evolutionary Computing and Computer Science - Mathematical Software
Abstract
The success of metaheuristic optimization methods has led to the development of a large variety of algorithm paradigms. However, no algorithm clearly dominates all its competitors on all problems. Instead, the underlying variety of landscapes of optimization problems calls for a variety of algorithms to solve them efficiently. It is thus of prior importance to have access to mature and flexible software frameworks which allow for an efficient exploration of the algorithm design space. Such frameworks should be flexible enough to accommodate any kind of metaheuristics, and open enough to connect with higher-level optimization, monitoring and evaluation softwares. This article summarizes the features of the ParadisEO framework, a comprehensive C++ free software which targets the development of modular metaheuristics. ParadisEO provides a highly modular architecture, a large set of components, speed of execution and automated algorithm design features, which are key to modern approaches to metaheuristics development. Comment: 12 pages, 6 figures, 3 listings, 1 table. To appear in 2021 Genetic and Evolutionary Computation Conference Companion (GECCO'21 Companion), July 10--14, 2021, Lille, France. ACM, New York, NY, USA
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we derive their optimal update at each iteration step. Not only provides this Bayesian framework a justification for the update of the CMA-ES algorithm but it also gives two new versions of CMA-ES either assuming normal-Wishart or normal-Inverse Wishart priors, depending whether we parametrize the likelihood by its covariance or precision matrix. We support our theoretical findings by numerical experiments that show fast convergence of these modified versions of CMA-ES. Comment: 10 pages, 9 figures
