This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

The combination of Evolutionary algorithms with local search was named "Memetic Algorithms" (MAs) in [1]. These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement [2]. In the case of MAs "memes" refer to the strategies (e.g. local refinement, perturbation or constructive methods, etc) that are employed to improve individuals. In this paper we review some works on the application of MAs to well known combinatorial optimisation problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of meta-heuristics it is possible to explore their design space and better understand their behaviour from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient Memetic Algorithms.

This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EA). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution. This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping GA on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping GA and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity. Keywords: evolutionary algorithms, genetic algorithms, constraint sati...

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This paper describes an investigation of solving Examination Timetabling Problems (ETTPs) with Genetic Algorithms (GAs) using a non-direct chromosome representation based on evolving the configuration of Constraint Satisfaction methods. There are two aims. The first is to circumvent the problems posed by a direct chromosome representation for the ETTP that consists of an array of events in which each value represents the timeslot which the corresponding event is assigned to. The second is to show that the adaptation of particular features in both the instance of the problem to be solved and the strategies used to solve it provides encouraging results for real ETTPs. There is much scope for investigating such approaches further, not only for the ETTP, but also for other related scheduling problems.

. This paper presents a new genetic local search algorithm for the graph coloring problem. The algorithm combines an original crossover based on the notion of union of independent sets and a powerful local search operator (tabu search). This new hybrid algorithm allows us to improve on the best known results of some large instances of the famous Dimacs benchmarks. 1 Introduction The graph coloring problem is one of the most studied NP-hard problems and can be defined informally as follows. Given an undirected graph, one wishes to color with a minimal number of colors the nodes of the graph in such a way that two colors assigned to two adjacent nodes must be different. Graph coloring has many practical applications such as timetabling and resource assignment. Given the NP-completeness of the coloring problem, it becomes natural to design heuristic methods. Indeed many heuristic methods have been developed, constructive methods in the 60's and 70's [1, 12], local search meta-heuristics ...

. This paper presents a Tabu Search (TS) algorithm for solving maximal constraint satisfaction problems. The algorithm was tested on a wide range of random instances (up to 500 variables and 30 values) . Comparisons were carried out with a min-conflicts+random-walk (MCRW) algorithm. Empirical evidence shows that the TS algorithm finds results which are better than that of the MCRW algorithm.the TS algorithm is 3 to 5 times faster than the MCRW algorithm to find solutions of the same quality. Keywords: Tabu search, constraint solving, combinatorial optimization. 1 Introduction A finite Constraint Network (CN) is composed of a finite set X of variables, a set D of finite domains and a set C of constraints over subsets of X. A constraint is a subset of the Cartesian product of the domains of the variables involved that specifies which combinations of values are compatible. A CN is said to be binary if all the constraints have 2 variables. Given a CN, the Constraint Satisfaction Problem ...

: In this paper, a generic tabu search is presented for three coloring problems: graph coloring, T-colorings and set T-colorings. This algorithm integrates important features such as greedy initialization, solution re-generation, dynamic tabu tenure, incremental evaluation of solutions and constraint handling techniques. Empirical comparisons show that this algorithm approaches the best coloring algorithms and outperforms some hybrid algorithms on a wide range of benchmarks. Experiments on large random instances of T-colorings and set T-colorings show encouraging results. 3.1 INTRODUCTION The graph coloring problem is one of the most studied NP-hard problems and can be defined informally as follows. Given an undirected graph, one wishes to color the nodes of the graph with a minimal number of colors in such a way that two colors assigned to two adjacent nodes must be di#erent, i.e., have a minimal distance greater than zero. Graph coloring has many practical applications such as timet...

The paper analyses recent developments of a number of memory-based metaheuristics such as taboo search, scatter search, genetic algorithms and ant colonies. It shows that the implementations of these general solving methods are more and more similar. So, a unified presentation is proposed under the name of Adaptive Memory Programming (AMP). A number of methods recently developed for the quadratic assignment, vehicle routing and graph colouring problems are reviewed and presented under the adaptive memory programming point of view. AMP presents a number of interesting aspects such as a high parallelization potential and the ability of dealing with real and dynamic applications.

Abstract. A type inclusion test is a procedure to decide whether two types are related by a given subtyping relationship. An efficient implementation of the type inclusion test plays an important role in the performance of object oriented programming languages with multiple subtyping like C++, Eiffel or Java. There are well-known methods for performing fast constant time type inclusion tests that use a hierarchical bit vector encoding of the partial ordered set representing the type hierarchy. The number of instructions required by the type inclusion test is proportional to the length of those bit vectors. We present a new algorithm based on graph coloring which computes a near optimal hierarchical encoding of type hierarchies. The new algorithm improves significantly on previous results – it is faster, simpler and generates smaller bit vectors. 1