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GECCO
2004
Springer
15 years 9 months 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»
15 years 10 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»
15 years 10 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»
15 years 8 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»
15 years 7 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