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.

Summary. The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters. 1

Abstract — Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) [with links to genetic programming (GP) and classifier systems (CS)], evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete. Index Terms — Classifier systems, evolution strategies, evolutionary computation, evolutionary programming, genetic algorithms,

Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that one could possibly enumerate 10 9 tours per second on a computer it would thus take roughly 10 639 years of computing to establish the optimality of this tour by exhaustive enumeration." This quote shows the real difficulty of a combinatorial optimization problem. The huge number of configurations is the primary difficulty when dealing with one of these problems. The quote belongs to M.W Padberg and M. Grotschel, Chap. 9., "Polyhedral computations", from the book The Traveling Salesman Problem: A Guided tour of Combinatorial Optimization [124]. It is interesting to compare the number of configurations of real-world problems in combinatorial optimization with those large numbers arising in Cosmol...

Within Genetic Algorithms (GAs) the mutation rate is mostly handled as a global, external parameter, which is constant over time or exogeneously changed over time. In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the search process. First experimental results are presented, which indicate that environment-- dependent self--adaptation of appropriate settings for the mutation rate is possible even for GAs. Furthermore, the reduction of the number of external parameters of a GA is seen as a first step towards achieving a problem--dependent self--adaptation of the algorithm. Introduction Natural evolution has proven to be a powerful mechanism for emergence and improvement of the living beings on our planet by performing a randomized search in the space of possible DNA-sequences. Due to this knowledge about the qualities of natural evolution, some resea...

. Evolutionary algorithms have been gaining increased attention the past few years because of their versatility and are being successfully applied in several different fields of study. We group under this heading a family of new computing techniques rooted in biological evolution that can be used for solving hard problems. In this chapter we present a survey of genetic algorithms and genetic programming, two important evolutionary techniques. We discuss their parallel implementations and some notable extensions, focusing on their potential applications in the field of evolvable hardware. 1 Introduction The performance of modern computers is quite impressive; it seems fair to say that computers are far better than humans in many domains and that they comprise a powerful tool that is constantly changing our view of the world. On scientific and engineering number-crunching problems performance increases steadily and we are able to tackle so-called "grand challenge" problems with gigaflop...

One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mutation. The existence of many different forms of crossover further complicates the issue. Despite theoretical analysis, it appears difficult to decide a priori which form of crossover to use, or even if crossover should be used at all. One possible solution to this difficulty is to have the EA be self-adaptive, i.e., to have the EA dynamically modify which forms of crossover to use and how often to use them, as it solves a problem. This paper describes an adaptive mechanism for controlling the use of crossover in an EA and explores the behavior of this mechanism in a number of different situations. An improvement to the adaptive mechanism is then presented. Surprisingly this improvement can a...

This paper focuses on the issue of evaluation of constraints handling methods, as the advantages and disadvantages of various methods are not well understood. The general way of dealing with constraints -- whatever the optimization method -- is by penalizing infeasible points. However, there are no guidelines on designing penalty functions. Some suggestions for evolutionary algorithms are given in [37], but they do not generalize. Other techniques that can be used to handle constraints in are more or less problem dependent. For instance, the knowledge about linear constraints can be incorporated into specific operators [24], or a repair operator can be designed that projects infeasible points onto feasible ones [30]

The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms.

This paper is devoted to the effects of fitness noise in EAs (evolutionary algorithms). After a short introduction to the history of this research field, the performance of GAs (genetic algorithms) and ESs (evolution strategies) on the hyper-sphere test function is evaluated. It will be shown that the main effects of noise -- the decrease of convergence velocity and the residual location error R1 -- are observed in both GAs and ESs.