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Many hands make light work: further studies in group evolution
Nicholas Tomko1, Inman Harvey, Nathaniel Virgo
1University of Sussex.
Artificial Life
|February 5, 2013
Summary
Standard genetic algorithms (GAs) struggle with complex tasks. This study introduces a group GA that promotes diversity and emergent niching for cooperative tasks without needing prior knowledge of subtask numbers.
Area of Science:
- Artificial Intelligence
- Evolutionary Computation
- Computational Biology
Background:
- Standard genetic algorithms (GAs) face challenges with tasks requiring niching or speciation due to genetic convergence.
- Existing evolutionary niching methods often require prior knowledge of the fitness landscape or are not suited for cooperative tasks where fitness is group-dependent.
Purpose of the Study:
- To address limitations of standard GAs in cooperative tasks by developing a group-based evolutionary algorithm for emergent niching.
- To present modified group GAs that do not require pre-specification of the number of niches.
Main Methods:
- Introduced the group GA, a group-based evolutionary algorithm designed to foster emergent niching in populations.
- Demonstrated the group GA on an immune system matching task.
- Developed two modified versions: random-group-size GA and evolved-group-size GA, to dynamically manage the number of niches.
Main Results:
- The group GA successfully demonstrated emergent niching on a cooperative task.
- The modified versions allow for evolution of groups without a priori knowledge of the number of subtasks or their distribution.
- Evolved-group-size GA optimizes the number of niches through evolution, enhancing adaptability.
Conclusions:
- The group GA framework provides a robust method for tackling complex, cooperative tasks in evolutionary computation.
- Emergent niching in group GAs reduces the need for specific fitness landscape assumptions.
- Dynamic adjustment of niche numbers (random or evolved) enhances the applicability of GAs to diverse, cooperative problems.
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