Adaptive Selection Operators in Evolutionary Algorithms
Department
Computer Science
Major
Computer Science
Research Advisor
Tauritz, Daniel R.
Advisor's Department
Computer Science
Funding Source
Opportunities for Undergraduate Research (OURE)
Abstract
Selection is a core genetic operator in many evolutionary algorithms (EAs). The performance of EAs on a given problem is dependent on properly configuring selection. A small set of common selection operators is used in the vast majority of EAs, typically fixed for the entire evolutionary run. Selecting which crossover operator to use and tuning its associated parameters to obtain acceptable performance on a specific problem is often a time consuming, manual process. Even then a custom selection operator may be required to achieve optimal performance. Finally, the best crossover configuration may be dependent on the state of the evolutionary run.
This work introduces Adaptive Selection Operators which address these shortcomings while relieving the user from the burden of selection operator configuration. Results are presented showing it to outperform the traditional selection operators k-tournament, truncation, and fitness proportionate selection on the Rosenbrock benchmark problem.
Biography
Nathaniel is currently a senior in Computer Science and an Undergraduate Research Assistant in the Natural Computation Laboratory.
Research Category
Sciences
Presentation Type
Poster Presentation
Document Type
Poster
Award
Sciences poster session, Second place
Location
Upper Atrium/Hall
Presentation Date
16 Apr 2014, 9:00 am - 11:45 am
Adaptive Selection Operators in Evolutionary Algorithms
Upper Atrium/Hall
Selection is a core genetic operator in many evolutionary algorithms (EAs). The performance of EAs on a given problem is dependent on properly configuring selection. A small set of common selection operators is used in the vast majority of EAs, typically fixed for the entire evolutionary run. Selecting which crossover operator to use and tuning its associated parameters to obtain acceptable performance on a specific problem is often a time consuming, manual process. Even then a custom selection operator may be required to achieve optimal performance. Finally, the best crossover configuration may be dependent on the state of the evolutionary run.
This work introduces Adaptive Selection Operators which address these shortcomings while relieving the user from the burden of selection operator configuration. Results are presented showing it to outperform the traditional selection operators k-tournament, truncation, and fitness proportionate selection on the Rosenbrock benchmark problem.
