Description

 Understand the fundamental principles and concepts of evolutionary computation  Comprehend the principles and techniques of genetic programming  Familiarize students with evolutionary strategies and evolutionary programming  Understand the principles and mechanisms of particle swarm optimization  Comprehend the principles and mechanisms of ant colony optimization  Understand the principles and strategies of differential evolution  Gain knowledge of multiobjective optimization concepts and techniques, apply evolutionary algorithms to solve multiobjective optimization problems  Comprehend the principles and coordination strategies of swarm robotics  Familiarize students with cultural algorithms and memetic computing  Comprehend the principles and techniques of artificial bee colony optimization  Understand the principles and mechanisms of coevolutionary algorithms  Familiarize students with hybrid evolutionary algorithms

Program

Artificial Intelligence 132 CRs

Objectives

• This course explores how principles from theories of evolution, natural selection, and swarming behavior can be used to construct machines that exhibit nontrivial behavior. In particular, the course covers techniques from evolutionary computation and swarm intelligence for developing software agents capable of solving problems as members of a larger population of agents. Specific topics addressed include representation and schemata; selection, reproduction, and recombination; theoretical models of computational intelligence; optimal allocation of trials (i.e., bandit problems); search, optimization, and machine learning; evolution of programs; population and swarm dynamics; and emergent behavior.