. Exploration and exploitation are the two cornerstones of problem solving by search. The common opinion about evolutionary algorithms is that they explore the search space by the (genetic) search operators, while exploitation is done by selection. This opinion is, however, questionable. In this paper we give a survey of different operators, review existing viewpoints on exploration and exploitation, and point out some discrepancies between and problems with current views. 1. Introduction Evolutionary algorithms (EA) belong to the family of stochastic generate-and-test search algorithms [28]. There are different types of EAs, the most common classification distinguishes Genetic Algorithms (GA), Evolution Strategies (ES) and Evolutionary Programming (EP), [4]. A fourth type of EA, Genetic Programming (GP) has grown out of GAs and is often seen as a sub-class of them. Besides the different historical roots and philosophy there are also technical differences between the three mai...

This paper introduces the notions of `quasi-species' and `error threshold' from molecular evolutionary biology. The error threshold is a critical mutation rate beyond which the effect of selection on the population changes drastically. We reproduce, using GAs --- and hence finite populations --- some interesting results obtained with an analytical model --- using infinite populations --- from the evolutionary biology literature. A reformulation of a previous analytical expression , which explicitly indicates the extent of the reduction in the error threshold as we move from infinite to finite populations, is derived. Error thresholds are shown to be lower for finite populations. Moreover, as in the infinite case, for low mutation rates recombination can reduce the diversity of the population and enhance overall fitness. For high mutation rates, however, recombination can push the population over the error threshold, and thereby cause a loss of genetic information. These results may be ...

Abstract—Sesame is a software framework that aims at developing a modeling and simulation environment for the efficient design space exploration of heterogeneous embedded systems. Since Sesame recognizes separate application and architecture models within a single system simulation, it needs an explicit mapping step to relate these models for cosimulation. The design tradeoffs during the mapping stage, namely, the processing time, power consumption, and architecture cost, are captured by a multiobjective nonlinear mixed integer program. This paper aims at investigating the performance of multiobjective evolutionary algorithms (MOEAs) on solving large instances of the mapping problem. With two comparative case studies, it is shown that MOEAs provide the designer with a highly accurate set of solutions in a reasonable amount of time. Additionally, analyses for different crossover types, mutation usage, and repair strategies for the purpose of constraints handling are carried out. Finally, a number of multiobjective optimization results are simulated for verification. Index Terms—Design space exploration, evolutionary algorithms, mixed integer programming, multiobjective optimization, multiprocessor system-on-chip (SoC) design. I.

This paper presents an approach to combine competent crossover and mutation operators via probabilistic model building. Both operators are based on the probabilistic model building procedure of the extended compact genetic algorithm (eCGA). The model sampling procedure of eCGA, which mimics the behavior of an idealized recombination— where the building blocks (BBs) are exchanged without disruption—is used as the competent crossover operator. On the other hand, a recently proposed BB-wise mutation operator—which uses the BB partition information to perform local search in the BB space—is used as the competent mutation operator. The resulting algorithm, called hybrid extended compact genetic algorithm (heCGA), makes use of the problem decomposition information for (1) effective recombination of BBs and (2) effective local search in the BB neighborhood. The proposed approach is tested on different problems that combine the core of three well known problem difficulty dimensions: deception, scaling, and noise. The results show that, in the absence of domain knowledge, the hybrid approach is more robust than either single-operatorbased approach.

Several factors contribute to making search problems easy or difficult. One of these factors is the modality of the fitness landscape. Quite often, when local search algorithms fail to locate the global optimum, it is because the algorithm converged to a local optimum. The majority of local search methods in use today maneuver through the search space using local neighborhood information around a single point to guide the search. In that search paradigm, the number of local optima that occur in the search space has a tremendous effect on search performance. Genetic

There is a great deal of information to be gained from studying the details within a GA run. This paper investigates the roles of crossover and mutation by observing the actions and effects of individual occurrences of each genetic operation. The observations are compared with some of the common expectations of these operators. 1 INTRODUCTION The most common way to test the usefulness of a genetic operator has typically been to compare the performance of a genetic algorithm (GA) with that operator to the performance of a GA without that operator. If the former is better, then the operator is said to be useful; if the latter is better, then the operator is considered not useful. While such "macroscopic" observations and comparisons reveal important information to the study of GAs, the "microscopic" details of a GA run also hold interesting information on how the GA works (Nordin and Banzhaf 1995; Spears and De Jong 1991; Spears 1993; Thierens and Goldberg 1993; Wu and Lindsay 1996). To...

Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select

In this paper, we present a novel approach based on genetic algorithms for performing camera calibration. Contrary to the classical nonlinear photogrammetric approach [1], the proposed technique can correctly find the near-optimal solution without the need of initial guesses (with only very loose parameter bounds) and with a minimum number of control points (7 points). Results from our extensive study using both synthetic and real image data as well as performance comparison with Tsai's procedure [2] demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness.

In this section we survey recombination operators that can apply more than two parents to create offspring. Some multi-parent recombination operators are defined for a fixed number of parents, e.g. have arity three, in some operators the number of parents is a random number that might be greater than two, and in yet other operators the arity is a parameter that can be set to an arbitrary integer number. We pay special attention to this latter type of operators and summarize results on the effect of operator arity on EA performance. 1 Introduction To make the coming survey unambiguous we have to start with setting some conventions on terminology. The term population will be used for a multiset of individuals that undergoes selection and reproduction. This terminology is maintained in genetic algorithms, evolutionary programming and genetic programming, but in evolution strategies all ¯ individuals in a (¯; ) or (¯ + ) strategy are called parents. We, however, use the term parents only ...

Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms of genetic recombination, based on phenomena like gene insertion, duplication or movement. The aim of this paper is to study one of these mechanisms: transposition. Transposition is a context-sensitive operator that promotes gene movement intra or inter chromosomes. This work presents an empirical study of the genetic algorithm performance, being the traditional crossover operator replaced by transposition. Such empirical study, based on an extensive set of test functions, shows that, under certain circumstances, transposition allows the GA to achieve higher quality solutions. 1 INTRODUCTION Genetic Algorithms (GA) are a search paradigm that applies ideas from evolutionary biology ...