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247
Perspectives: Complex Adaptations and the Evolution of Evolvability
, 1996
"... The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian p ..."
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Cited by 198 (8 self)
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The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally effective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess "evolvability", i.e. the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotypephenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variation, which are the actually realized differences between individuals. The genotypephenotype map is the ...
MAXMIN Ant System
, 1999
"... Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more finetuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Sa ..."
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Cited by 122 (3 self)
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Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more finetuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this ares has mainly focused on the development of algorithmic variants which achieve better performance than AS. In this article, we present¨�©� � –¨��� � Ant System, an Ant Colony Optimization algorithm derived from Ant System.¨�©� � –¨��� � Ant System differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to¨� ¨ AS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that ¨�©� � –¨��� � Ant System is currently among the best performing algorithms for these problems.
Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem
, 1999
"... In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed and the results are used to classify problem instances according to their hardness for local search heuristics and metaheuristics based on local search. The local properties of t ..."
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Cited by 84 (9 self)
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In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed and the results are used to classify problem instances according to their hardness for local search heuristics and metaheuristics based on local search. The local properties of the tness landscape are studied by performing an autocorrelation analysis, while the global structure is investigated by employing a fitness distance correlation analysis. It is shown that epistasis, as expressed by the dominance of the flow and distance matrices of a QAP instance, the landscape ruggedness in terms of the correlation length of a landscape, and the correlation between fitness and distance of local optima in the landscape together are useful for predicting the performance of memetic algorithms  evolutionary algorithms incorporating local search  to a certain extent. Thus, based on these properties a favorable choice of recombination and/or mutation operators can be found.
GenProg: A Generic Method for Automatic Software Repair
"... Abstract—This paper describes GenProg, an automated method for repairing defects in offtheshelf, legacy programs without formal specifications, program annotations, or special coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required f ..."
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Cited by 70 (2 self)
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Abstract—This paper describes GenProg, an automated method for repairing defects in offtheshelf, legacy programs without formal specifications, program annotations, or special coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given defect, using existing test suites to encode both the defect and required functionality. Structural differencing algorithms and delta debugging reduce the difference between this variant and the original program to a minimal repair. We describe the algorithm and report experimental results of its success on 16 programs totaling 1.25 M lines of C code and 120K lines of module code, spanning eight classes of defects, in 357 seconds, on average. We analyze the generated repairs qualitatively and quantitatively to demonstrate that the process efficiently produces evolved programs that repair the defect, are not fragile input memorizations, and do not lead to serious degradation in functionality. Index Terms—Automatic programming, corrections, testing and debugging. Ç 1
Fitness Landscapes and Memetic Algorithm Design
 New Ideas in Optimization
, 1999
"... Introduction The notion of fitness landscapes has been introduced to describe the dynamics of evolutionary adaptation in nature [40] and has become a powerful concept in evolutionary theory. Fitness landscapes are equally well suited to describe the behavior of heuristic search methods in optimizat ..."
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Cited by 70 (8 self)
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Introduction The notion of fitness landscapes has been introduced to describe the dynamics of evolutionary adaptation in nature [40] and has become a powerful concept in evolutionary theory. Fitness landscapes are equally well suited to describe the behavior of heuristic search methods in optimization, since the process of evolution can be thought of as searching a collection of genotypes in order to find the genotype of an organism with highest fitness and thus highest chance of survival. Thinking of a heuristic search method as a strategy to "navigate" in the fitness landscape of a given optimization problem may help in predicting the performance of a heuristic search algorithm if the structure of the landscape is known in advance. Furthermore, the analysis of fitness landscapes may help in designing highly effective search algorithms. In the following we show how the analysis of fitness landscapes of combinatorial optimization problems can aid in designing the components of
ACO Algorithms for the Traveling Salesman Problem
 Periaux (eds), Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications
, 1999
"... Ant algorithms [18, 14, 19] are a recently developed, populationbased approach which has been successfully applied to several NPhard combinatorial ..."
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Cited by 63 (6 self)
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Ant algorithms [18, 14, 19] are a recently developed, populationbased approach which has been successfully applied to several NPhard combinatorial
Rigorous Hitting Times for Binary Mutations
, 1999
"... In the binary evolutionary optimization framework, two mutation operators are theoretically investigated. For both the standard mutation, in which all bits are flipped independently with the same probability, and the 1bitflip mutation, which flips exactly one bit per bitstring, the statistical dis ..."
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Cited by 62 (2 self)
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In the binary evolutionary optimization framework, two mutation operators are theoretically investigated. For both the standard mutation, in which all bits are flipped independently with the same probability, and the 1bitflip mutation, which flips exactly one bit per bitstring, the statistical distribution of the first hitting times of the target are thoroughly computed (expectation and variance) up to terms of order l (the size of the bitstrings) in two distinct situations: without any selection, or with the deterministic (1+1)ES selection on the OneMax problem. In both cases, the 1bitflip mutation convergence time is smaller by a constant (in terms of l) multiplicative factor. These results extend to the case of multiple independent optimizers. Keywords Evolutionary algorithms, stochastic analysis, binary mutations, Markov chains, hitting times. 1 Introduction One known drawback of Evolutionary Algorithms as function optimizers is the amount of computational efforts they re...
Iterated Local Search for the Quadratic Assignment Problem
 FG INTELLEKTIK, FB INFORMATIK
, 1999
"... Iterated local search (ILS) is a surprisingly simple but at the same time powerful metaheuristic for finding high quality approximate solutions for combinatorial optimization problems. ILS is based on the repeated application of a local search algorithm to initial solution which are obtained by m ..."
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Cited by 60 (10 self)
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Iterated local search (ILS) is a surprisingly simple but at the same time powerful metaheuristic for finding high quality approximate solutions for combinatorial optimization problems. ILS is based on the repeated application of a local search algorithm to initial solution which are obtained by mutations of previously found local optima  in most ILS algorithms these mutations are applied to the best found solution since the start of the search. In this article we present and analyze the application of ILS to the quadratic assignment problem (QAP). We first justify the potential usefulness of an ILS approach to this problem by an analysis of the QAP search space. An investigation of the runtime behavior of the ILS algorithm reveals a stagnation behavior of the algorithm  it may get stuck for many iterations in local optima. To avoid such stagnation situations we propose enhancements of the ILS algorithm based on extended acceptance criteria as well as populationbased...
SEARCH, polynomial complexity, and the fast messy genetic algorithm
, 1995
"... Blackbox optimizationoptimization in presence of limited knowledge about the objective functionhas recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Si ..."
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Cited by 58 (10 self)
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Blackbox optimizationoptimization in presence of limited knowledge about the objective functionhas recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Simulated annealing, genetic algorithms, tabu search are some examples. Unfortunately, each of these algorithms is creating a separate field in itself and their use in practice is often guided by personal discretion rather than scientific reasons. The primary reason behind this confusing situation is the lack of any comprehensive understanding about blackbox search. This dissertation takes a step toward clearing some of the confusion. The main objectives of this dissertation are: 1. present SEARCH (Search Envisioned As Relation & Class Hierarchizing)an alternate perspective of blackbox optimization and its quantitative analysis that lays the foundation essential for transcending the limits of random enumerative search; 2. design and testing of the fast messy genetic algorithm. SEARCH is a general framework for understanding blackbox optimization in terms of relations,
Fitness distance correlation analysis: An instructive counterexample
 In Proceedings of the Seventh International Conference on Genetic Algorithms
, 1997
"... Fitness distance correlation (FDC) has been offered as a summary statistic with apparent success in predicting the performance of genetic algorithms for global optimization. Here, a counterexample to Hammingdistance based FDC is examined for what it reveals about how GAs work. The counterexample is ..."
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Cited by 55 (0 self)
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Fitness distance correlation (FDC) has been offered as a summary statistic with apparent success in predicting the performance of genetic algorithms for global optimization. Here, a counterexample to Hammingdistance based FDC is examined for what it reveals about how GAs work. The counterexample is a fitness function that is ‘GAeasy ’ for global optimization, but which shows no relationship between fitness and Hamming distance from the global optimum. Fitness is a function that declines with the number of switches between 0 and 1 along the bitstring. The test function is ‘GAeasy’, in that a GA using only singlepoint crossover can find the global optimum with a sample on the order of 10 ¡ 3 to 10 ¡ 9 of the points in the search space, an efficiency which increases with the size of the search space. This result confirms the suspicion that predictors for genetic algorithm performance are vulnerable if they are based on arbitrary properties of the search space, and not the actual dynamics of the genetic algorithm. The test function’s solvability by a GA is accurately predicted, however, by another property—its evolvability, the probability that the genetic operator produces offspring that are fitter than their parents. It is also accurately predicted by FDC that uses a distance measure defined by the crossover operator itself, instead of Hamming distance. Mutationbased distance measures are also investigated, and are found to predict the GA’s performance when mutation is the only genetic operator acting. A comparison is made between Hammingdistance based FDC analysis, crossoverdistance based FDC analysis, evolvability analysis, and other methods of predicting GA performance. 1