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GECCO
2004
Springer
14 years 23 days ago
How Are We Doing? Predicting Evolutionary Algorithm Performance
Abstract. Given an evolutionary algorithm for a problem and an instance of the problem, the results of several trials of the EA on the instance constitute a sample from the distrib...
Mark A. Renslow, Brenda Hinkemeyer, Bryant A. Juls...
GECCO
2007
Springer
132views Optimization» more  GECCO 2007»
14 years 1 months ago
Empirical analysis of ideal recombination on random decomposable problems
This paper analyzes the behavior of a selectorecombinative genetic algorithm (GA) with an ideal crossover on a class of random additively decomposable problems (rADPs). Specifical...
Kumara Sastry, Martin Pelikan, David E. Goldberg
GECCO
2007
Springer
177views Optimization» more  GECCO 2007»
14 years 1 months ago
On the behavioral diversity of random programs
Generating a random sampling of program trees with specified function and terminal sets is the initial step of many program evolution systems. I present a theoretical and experim...
Moshe Looks
GECCO
1999
Springer
133views Optimization» more  GECCO 1999»
13 years 11 months ago
Non-Standard Crossover for a Standard Representation - Commonality-Based Feature Subset Selection
The Commonality-Based Crossover Framework has been presented as a general model for designing problem specific operators. Following this model, the Common Features/Random Sample ...
Stephen Y. Chen, Cesar Guerra-Salcedo, Stephen F. ...
GECCO
2006
Springer
155views Optimization» more  GECCO 2006»
13 years 11 months ago
Comparison of genetic representation schemes for scheduling soft real-time parallel applications
This paper presents a hybrid technique that combines List Scheduling (LS) with Genetic Algorithms (GA) for constructing non-preemptive schedules for soft real-time parallel applic...
Yoginder S. Dandass, Amit C. Bugde