An Arithmetic Test Suite for Genetic Programming
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Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.
History
The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.
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1969-present
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- College of Liberal Arts and Sciences (parent college)
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Abstract
In this paper we explore a number of ideas for enhancing the techniques of genetic programming in the context of a very simple test environment that nevertheless possesses some degree of algorithmic subtlety. We term this genetic programming environment plus-one-recall-store (PORS). This genetic programming environment is quite simple having only a pair of terminals and a pair of operations. The terminals are the number one and recall from an external memory. The operations are a unary store operation and binary addition, +, on natural numbers. In this paper we present the PORS environment, present a mathematical description of its properties, and then focus on testing the use of Markov chains in generating, crossing over, and mutating evolving programs. We obtain a surprising indication of the correct situations in which to use Markov chains during evolutionary program induction.