Hampshire College

About: Hampshire College is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Genetic programming & Population. The organization has 461 authors who have published 998 publications receiving 40827 citations.

The Basic Skills

Abstract: This chapter focuses on the number system The number system is a decimal system because it is based on 10 Any number can be written using the proper choice and combination of 10 symbols—0 through 9 The decimal system is a place value system This means that the value of each digit depends in part on its position within the number On moving to the left in a decimal number, each place is worth 10 times the previous place; on moving to the right, each place is worth one-tenth of the previous place A rounding variation that is found in other studies is the computer rule, which can avoid errors in rounding large series of numbers By means of this rule, some numbers may become larger while others become smaller The correct alignment of numbers around the decimal point is essential in successful addition To accomplish this, the numbers must be put in a column by lining up the decimal points and by filling in zeroes for any numbers with fewer decimal digits than the longest number

Evolving a digital multiplier with the pushgp genetic programming system

Abstract: A recent article on benchmark problems for genetic programming suggested that researchers focus attention on the digital multiplier problem, also known as the "multiple output multiplier" problem, in part because it is scalable and in part because the requirement of multiple outputs presents challenges for some forms of genetic programming [20]. Here we demonstrate the application of stack-based genetic programming to the digital multiplier problem using the PushGP genetic programming system, which evolves programs expressed in the stack-based Push programming language. We demonstrate the use of output instructions and argue that they provide a natural mechanism for producing multiple outputs in a stack-based genetic programming context. We also show how two recent developments in PushGP dramatically improve the performance of the system on the digital multiplier problem. These developments are the "ULTRA" genetic operator, which produces offspring via "Uniform Linear Transformation with Repair and Alternation" [12], and "lexicase selection," which selects parents according to performance on cases considered sequentially in random order [11]. Our results using these techniques show not only their utility, but also the utility of the digital multiplier problem as a benchmark problem for genetic programming research. The results also demonstrate the exibility of stack-based genetic programming for solving problems with multiple outputs and for serving as a platform for experimentation with new genetic programming techniques.