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GECCO
2004
Springer
13 years 10 months ago
Hybridizing Evolutionary Testing with the Chaining Approach
Fitness functions derived for certain white-box test goals can cause problems for Evolutionary Testing (ET), due to a lack of sufficient guidance to the required test data. Often t...
Phil McMinn, Mike Holcombe
GECCO
2004
Springer
117views Optimization» more  GECCO 2004»
13 years 10 months ago
Comparing Search Algorithms for the Temperature Inversion Problem
Several inverse problems exist in the atmospheric sciences that are computationally costly when using traditional gradient based methods. Unfortunately, many standard evolutionary ...
Monte Lunacek, L. Darrell Whitley, Philip Gabriel,...
GECCO
2004
Springer
175views Optimization» more  GECCO 2004»
13 years 10 months ago
An Architecture for Massive Parallelization of the Compact Genetic Algorithm
This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The approach is scalable, has low synchronization costs, and i...
Fernando G. Lobo, Cláudio F. Lima, Hugo Mar...
GECCO
2004
Springer
13 years 10 months ago
A Philosophical Essay on Life and Its Connections with Genetic Algorithms
This paper makes a number of connections between life and various facets of genetic and evolutionary algorithms research. Specifically, it addresses the topics of adaptation, mult...
Fernando G. Lobo
GECCO
2004
Springer
109views Optimization» more  GECCO 2004»
13 years 10 months ago
Development of a Genetic Algorithm for Optimization of Nanoalloys
A genetic algorithm has been developed in order to find the global minimum of platinum-palladium nanoalloy clusters. The effect of biasing the initial population and predating sp...
Lesley D. Lloyd, Roy L. Johnston, Said Salhi
GECCO
2004
Springer
13 years 10 months ago
Mixed Decision Trees: Minimizing Knowledge Representation Bias in LCS
Xavier Llorà, Stewart W. Wilson
GECCO
2004
Springer
175views Optimization» more  GECCO 2004»
13 years 10 months ago
Enhanced Innovation: A Fusion of Chance Discovery and Evolutionary Computation to Foster Creative Processes and Decision Making
Abstract. Human-based genetic algorithms are powerful tools for organizational modeling. If we enhance them using chance discovery techniques, we obtain an innovative approach for ...
Xavier Llorà, Kei Ohnishi, Ying-Ping Chen, ...
GECCO
2004
Springer
125views Optimization» more  GECCO 2004»
13 years 10 months ago
An Island-Based GA Implementation for VLSI Standard-Cell Placement
Genetic algorithms require relatively large computation time to solve optimization problems, especially in VLSI CAD such as module placement. Therefore, island-based parallel GAs a...
Guangfa Lu, Shawki Areibi