Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows

Selected papers at Learning and Intelligent Optimization: Third International Conference, LION 3, Trento, Italy, January 14-18, 2009Beam-ACO algorithms are hybrid methods that combine the metaheuristic ant colony optimization with beam search. They heavily rely on accurate and computationally inexpensive bounding information for choosing between different partial solutions during the solution construction process. In this work we present the use of stochastic sampling as a useful alternative to bounding information in cases were computing accurate bounding information is too expensive. As a case study we choose the well-known travelling salesman problem with time windows. Our results clearly demonstrate that Beam-ACO, even when bounding information is replaced by stochastic sampling, may have important advantages over standard ACO algorithms.Peer ReviewedPostprint (published version

Neutral Fitness Landscape in the Cellular Automata Majority Problem

International audienceWe study in detail the fitness landscape of a difficult cellular automata computational task: the majority problem. Our results show why this problem landscape is so hard to search, and we quantify the large degree of neutrality found in various ways. We show that a particular subspace of the solution space, called the "Olympus", is where good solutions concentrate, and give measures to quantitatively characterize this subspace

Optimization as Side-Effect of Evolving Allelopathic Diversity

. Many bacteria carry gene complexes that code for a toxin-antidote pair, e.g. colicin systems. Such gene complexes can be advantageous for its host by killing competitor bacteria while the antidote protects the host. However, in order to evolve a novel and useful toxin first a proper antidote must be evolved. We present a model of bacteria that can express and evolve such allelopathic systems. Although in the model novel types must evolve from existing types we find that nevertheless in general a high diversity of toxins evolves and, as a sideeffect thereof, generalized immunity mechanisms. We interpret the allelopathic systems in terms of an optimization problem: fitness cases are toxins and solutions present (potential) antidotes. As a side-effect of the evolution of allelopathic systems generalized solutions of the optimization task are evolved as well. 1 Introduction Many bacteria, such as Escherichia Coli and related bacteria, carry colicin systems [7, 17]. Colicin systems are g..