Sciweavers

KBSE
2007
IEEE
14 years 5 months ago
Nighthawk: a two-level genetic-random unit test data generator
Randomized testing has been shown to be an effective method for testing software units. However, the thoroughness of randomized unit testing varies widely according to the settin...
James H. Andrews, Felix Chun Hang Li, Tim Menzies
IJCNN
2007
IEEE
14 years 5 months ago
Parallel Learning of Large Fuzzy Cognitive Maps
— Fuzzy Cognitive Maps (FCMs) are a class of discrete-time Artificial Neural Networks that are used to model dynamic systems. A recently introduced supervised learning method, wh...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
CIRA
2007
IEEE
152views Robotics» more  CIRA 2007»
14 years 5 months ago
Co-Evolution of Sensor Morphology and Control on a Simulated Legged Robot
— This paper discusses utilizing genetic algorithms to automatically design a suitable sensor morphology and controller for a given task in categories of environments. The type o...
Gary B. Parker, Pramod J. Nathan
ICNSC
2008
IEEE
14 years 5 months ago
Multiple Sequence Alignment Based on Genetic Algorithms with Reserve Selection
— This paper presents an approach to the multiple sequence alignment (MSA) problem by applying genetic algorithms with a reserve selection mechanism. MSA is one of the most funda...
Yang Chen, Jinglu Hu, Kotaro Hirasawa, Songnian Yu
DEXAW
2008
IEEE
119views Database» more  DEXAW 2008»
14 years 5 months ago
Evolutionary Approaches to Linear Ordering Problem
Linear Ordering Problem (LOP) is a well know optimization problem attractive for its complexity (it is a NPhard problem), rich collection of testing data and variety of real world...
Václav Snásel, Pavel Krömer, Ja...
CEC
2008
IEEE
14 years 5 months ago
Hyper-selection in dynamic environments
— In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods...
Shengxiang Yang, Renato Tinós
CEC
2008
IEEE
14 years 5 months ago
Mesh dependency of stress-based crossover for structural topology optimization
— This paper presents a genetic algorithm (GA) with a stress-based crossover (SX) operator to obtain a solution without checkerboard patterns for multi-constrained topology optim...
Cuimin Li, Tomoyuki Hiroyasu, Mitsunori Miki
ICNC
2009
Springer
14 years 5 months ago
Reducing Boarding Time: Synthesis of Improved Genetic Algorithms
—With the aim to minimize boarding time and devise procedures for boarding strategies, this paper develop the synthesis of Improved Genetic Algorithms and simulation. This paper ...
Kang Wang
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
14 years 5 months ago
Three interconnected parameters for genetic algorithms
When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria...
Pedro A. Diaz-Gomez, Dean F. Hougen
GECCO
2009
Springer
109views Optimization» more  GECCO 2009»
14 years 5 months ago
Crossover operators for multiobjective k-subset selection
Genetic algorithms are often applied to combinatorial optimization problems, the most popular one probably being the traveling salesperson problem. In contrast to permutations use...
Thorsten Meinl, Michael R. Berthold