Dynamic selection of evolutionary algorithm operators based on online learning and fitness landscape metrics

Abstract. Self-adaptive mechanisms for the identification of the most suitable variation operator in Evolutionary meta-heuristics rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel Adaptive Operator Selection mechanism which uses a set of four Fitness Landscape Analysis techniques and an online learning al-gorithm, Dynamic Weighted Majority, to provide more detailed infor-mations about the search space in order to better determine the most suitable crossover operator on a set of Capacitated Arc Routing Prob-lem (CARP) instances. Extensive comparison with a state of the art approach has proved that this technique is able to produce comparable results on the set of benchmark problems.

Proposta de classificação hierarquizada dos modelos de solução para o problema de job shop scheduling A proposition of hierarchical classification for solution models in the job shop scheduling problem

Este artigo propõe uma classificação hierarquizada dos modelos utilizados na solução do problema de programação da produção intermitente do tipo job shop, incluindo tanto os que fornecem solução ótima, quanto os modelos heurísticos mais recentes baseados em métodos de busca estendida. Por meio dessa classificação obteve-se um painel amplo dos modelos existentes, evidenciando as diferentes abordagens do problema e suas soluções, com o objetivo de proporcionar uma orientação preliminar na escolha do modelo de job shop scheduling mais adequado.<br>This paper proposes a hierarchical model classification used in the job shop scheduling problem, including those that provide an optimal solution and the more recent ones based on heuristics, called extended search methods. A panel with the existing models is obtained by this classification, and solutions and approach differences are highlighted with the aim of a preliminary orientation on the choice of a more adequate job shop scheduling model

Joint rolling-horizon scheduling of materials processing and lot-sizing with sequence-dependent setups

A lot sizing and scheduling problem from a foundry is considered in which key materials are produced and then transformed into many products on a single machine. A mixed integer programming (MIP) model is developed, taking into account sequence-dependent setup costs and times, and then adapted for rolling horizon use. A relax-and-fix (RF) solution heuristic is proposed and computationally tested against a high-performance MIP solver. Three variants of local search are also developed to improve the RF method and tested. Finally the solutions are compared with those currently practiced at the foundry. © 2007 Springer Science+Business Media, LLC