Results 1 - 10
of
624
Parameter control in evolutionary algorithms
- IEEE Transactions on Evolutionary Computation
"... 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 classifica ..."
Abstract
-
Cited by 365 (42 self)
- Add to MetaCart
(Show Context)
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
Automatically Finding Patches Using Genetic Programming ∗
"... Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specif ..."
Abstract
-
Cited by 146 (24 self)
- Add to MetaCart
(Show Context)
Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report experimental results demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average. 1
Design patterns from biology for distributed computing
- ACM TRANS. AUTON. ADAPT. SYST
, 2006
"... ..."
(Show Context)
GenProg: A Generic Method for Automatic Software Repair
"... Abstract—This paper describes GenProg, an automated method for repairing defects in off-the-shelf, 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 ..."
Abstract
-
Cited by 73 (3 self)
- Add to MetaCart
(Show Context)
Abstract—This paper describes GenProg, an automated method for repairing defects in off-the-shelf, 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
Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters
, 2007
"... The main objective of this paper is to present and evaluate a method that helps to calibrate the parameters of an evolutionary algorithm in a systematic and semi-automated manner. The method for Relevance Estimation and Value Calibration of EA parameters (REVAC) is empirically evaluated in two diffe ..."
Abstract
-
Cited by 55 (13 self)
- Add to MetaCart
The main objective of this paper is to present and evaluate a method that helps to calibrate the parameters of an evolutionary algorithm in a systematic and semi-automated manner. The method for Relevance Estimation and Value Calibration of EA parameters (REVAC) is empirically evaluated in two different ways. First, we use abstract test cases reflecting the typical properties of EA parameter spaces. Here we observe that REVAC is able to approximate the exact (hand-coded) relevance of parameters and it works robustly with measurement noise that is highly variable and not normally distributed. Second, we use REVAC for calibrating GAs for a number of common objective functions. Here we obtain a common sense validation, REVAC finds mutation rate pm much more sensitive than crossover rate pc and it recommends intuitively sound values: pm between 0.01 and 0.1, and 0.6 ≤ pc ≤ 1.0. 1
Evolutionary algorithm with the guided mutation for the maximum clique problem
- IEEE Transactions on Evolutionary Computation
, 2005
"... Abstract—Estimation of distribution algorithms sample new solutions (offspring) from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The location information of solutions found so far (i.e., the actual positions of these solutio ..."
Abstract
-
Cited by 44 (15 self)
- Add to MetaCart
(Show Context)
Abstract—Estimation of distribution algorithms sample new solutions (offspring) from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The location information of solutions found so far (i.e., the actual positions of these solutions in the search space) is not directly used for generating offspring in most existing estimation of distribution algorithms. This paper introduces a new operator, called guided mutation. Guided mutation generates offspring through combination of global statistical information and the location information of solutions found so far. An evolutionary algorithm with guided mutation (EA/G) for the maximum clique problem is proposed in this paper. Besides guided mutation, EA/G adopts a strategy for searching different search areas in different search phases. Marchiori’s heuristic is applied to each new solution to produce a maximal clique in EA/G. Experimental results show that EA/G outperforms the heuristic genetic algorithm of Marchiori (the best evolutionary algorithm reported so far) and a MIMIC algorithm on DIMACS benchmark graphs. Index Terms—Estimation of distribution algorithms, evolutionary algorithm, guided mutation, heuristics, hybrid genetic algorithm, maximum clique problem (MCP). I.
Evolutionary algorithms with on-the-fly population size adjustment
- Parallel Problem Solving from Nature PPSN VIII, LNCS 3242
, 2004
"... Abstract. In this paper we evaluate on-the-fly population (re)sizing mechanisms for evolutionary algorithms (EAs). Evaluation is done by an experimental comparison, where the contestants are various existing methods and a new mechanism, introduced here. These comparisons consider EA performance in t ..."
Abstract
-
Cited by 35 (3 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper we evaluate on-the-fly population (re)sizing mechanisms for evolutionary algorithms (EAs). Evaluation is done by an experimental comparison, where the contestants are various existing methods and a new mechanism, introduced here. These comparisons consider EA performance in terms of success rate, speed, and solution quality, measured on a variety of fitness landscapes. These landscapes are created by a generator that allows for gradual tuning of their characteristics. Our test suite covers a wide span of landscapes ranging from a smooth one-peak landscape to a rugged 1000-peak one. The experiments show that the population (re)sizing mechanisms exhibit significant differences in speed, measured by the number of fitness evaluations to a solution and the best EAs with adaptive population resizing outperform the traditional genetic algorithm (GA) by a large margin. 1
Tuning Search Algorithms for Real-World Applications: A Regression Tree Based Approach
, 2004
"... The optimization of complex real-world problems might benefit from well tuned algorithm's parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis ..."
Abstract
-
Cited by 34 (5 self)
- Add to MetaCart
The optimization of complex real-world problems might benefit from well tuned algorithm's parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis, design and analysis of computer experiments methods, and tree-based regression. It can also be applied to analyze the influence of different operators or to compare the performance of different algorithms. An evolution strategy and a simulated annealing algorithm that optimize an elevator supervisory group controller system are used to demonstrate the applicability of our approach to real-world optimization problems.
A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems
- IEEE Transactions on Evolutionary computation
, 2003
"... This paper presents a simple multimembered evolution strategy (SMES) to solve global nonlinear optimization problems. The approach does not require the use of a penalty function and it does not require any extra parameters (besides those used with an evolution strategy). Instead, it uses a simple di ..."
Abstract
-
Cited by 32 (5 self)
- Add to MetaCart
(Show Context)
This paper presents a simple multimembered evolution strategy (SMES) to solve global nonlinear optimization problems. The approach does not require the use of a penalty function and it does not require any extra parameters (besides those used with an evolution strategy). Instead, it uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population This technique helps the algorithm to find the global optimum despite reaching reasonably fast the feasible region of the search space. Some simple selection criteria are used to guide the process to the feasible region of the search space. Also, the initial step size of the evolution strategy is reduced in order to perform a finer search and a combined (discrete/intermediate) recombination technique improves its exploitation capabilities. The approach was tested with a well-known benchmark. The results obtained are very competitive, when comparing the proposed approach against other state-of-the art techniques and its computational cost (measured by the number of fitness function evaluations) is lower than the required cost of the other techniques compared. 1
