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Emergence of Collective Behaviors in Hub-Based Colonies using Grammatical Evolution

Aadesh Neupane
MS Thesis Proposal

Tuesday, February 13, 9:00 AM
3350 TMCB
Advisor: David Wingate
 

 

Animals such as bees, ants, birds, fish, and others are able to efficiently perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators without centralized control or coordination. These complex collective behaviors are the result of emergence. Conventionally, mimicking these collective behaviors with robots requires researchers to study actual behaviors, derive mathematical models, and implement these models as algorithms. Since the conventional approach is very time consuming and cumbersome, we purpose an emergence-based method for the efficient evolution of collective behaviors.

Our purposed method, Grammatical Evolution algorithm for Evolution of Swarm bEhaviors (GEESE), is based on grammatical evolution, that extends the literature on using genetic methods to generate collective behaviors for robot swarms.
GEESE uses grammatical evolution to evolve a primitive set of human-provided rules, represented in a BNF grammar, into productive individual behaviors. We propose to show that GEESE is generic enough, given an initial grammar, that it can be applied to evolve collective behaviors for multiple problems with just a minor change in objective function.

Proposed validation of GEESE is as follows: First, GEESE is compared with state-of-the-art genetic algorithms on the canonical Santa Fe Trail problem. Results show that GEESE outperforms the state-of-the-art by a)~providing better solutions given sufficient population size while b)~utilizing fewer evolutionary steps. Second, GEESE will be used to evolve collective swarm behavior for a foraging task. We propose that evolved the foraging behavior using GEESE will outperform both hand-coded solutions as well as solutions generated by conventional Grammatical Evolution. Third, we will show that with a minor change to the objective function, the same set of the primitive rules can evolve solutions to the nest-maintenance and cooperative transport collective behavior problems.
 




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