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SEMCCO
2010
8 years 11 months ago
Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies
Differential Evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However, the performance of DE is sensitive ...
Rammohan Mallipeddi, Ponnuthurai Nagaratnam Sugant...
SSIRI
2010
8 years 12 months ago
A Formal Framework for Mutation Testing
— Model-based approaches, especially based on directed graphs (DG), are becoming popular for mutation testing as they enable definition of simple, nevertheless powerful, mutation...
Fevzi Belli, Mutlu Beyazit
ICST
2010
IEEE
8 years 12 months ago
(Un-)Covering Equivalent Mutants
—Mutation testing measures the adequacy of a test suite by seeding artificial defects (mutations) into a program. If a test suite fails to detect a mutation, it may also fail to...
David Schuler, Andreas Zeller
ENTCS
2002
152views more  ENTCS 2002»
9 years 1 months ago
Contract-based mutation testing in the refinement calculus
This article discusses mutation testing strategies in the context of refinement. Here, a novel generalization of mutation testing techniques is presented to be applied to contract...
Bernhard K. Aichernig
ALIFE
2004
9 years 1 months ago
Evolution of Robustness in Digital Organisms
We study the evolution of robustness in digital organisms adapting to a high mutation rate. As genomes adjust to the harsh mutational environment, the mean effect of single mutatio...
Jeffrey A. Edlund, Christoph Adami
ICGA
1993
157views Optimization» more  ICGA 1993»
9 years 2 months ago
Optimal Interaction of Mutation and Crossover in the Breeder Genetic Algorithm
The dynamic behavior of mutation and crossover is investigated with the Breeder Genetic Algorithm. The main emphasis is on binary functions. The genetic operators are compared nea...
Heinz Mühlenbein, Dirk Schlierkamp-Voosen
DMIN
2006
126views Data Mining» more  DMIN 2006»
9 years 2 months ago
Comparison and Analysis of Mutation-based Evolutionary Algorithms for ANN Parameters Optimization
Mutation-based Evolutionary Algorithms, also known as Evolutionary Programming (EP) are commonly applied to Artificial Neural Networks (ANN) parameters optimization. This paper pre...
Kristina Davoian, Alexander Reichel, Wolfram-Manfr...
GECCO
2006
Springer
218views Optimization» more  GECCO 2006»
9 years 5 months ago
A survey of mutation techniques in genetic programming
The importance of mutation varies across evolutionary computation domains including: genetic programming, evolution strategies, and genetic algorithms. In the genetic programming ...
Alan Piszcz, Terence Soule
EPS
1997
Springer
9 years 5 months ago
An Individually Variable Mutation-Rate Strategy for Genetic Algorithms
Abstract. In Neo-Darwinism, mutation can be considered to be unaffected by selection pressure. This is the metaphor generally used by the genetic algorithm for its treatment of the...
Stephen A. Stanhope, Jason M. Daida
SIGGRAPH
1997
ACM
9 years 5 months ago
Metropolis light transport
We present a new Monte Carlo method for solving the light transport problem, inspired by the Metropolis sampling method in computational physics. To render an image, we generate a...
Eric Veach, Leonidas J. Guibas
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