By Topic

A family of compact genetic algorithms for intrinsic evolvable hardware

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Gallagher, J.C. ; Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA ; Vigraham, S. ; Kramer, G.

For many evolvable hardware applications, small size and power efficiency are critical design considerations. One manner in which significant memory, and thus, power and space savings can be realized in a hardware-based evolutionary algorithm is to represent populations of candidate solutions as probability vectors rather than as sets of bit strings. The compact genetic algorithm (CGA) is a probability vector-based evolutionary algorithm that can be efficiently and elegantly implemented in digital hardware. Unfortunately, the CGA is a very weak, first order, evolutionary algorithm that is unlikely to possess sufficient search power to enable intrinsic evolvable hardware applications. In this paper, we further develop a number of modifications to the basic CGA that significantly improve its search efficacy without substantially increasing the size and complexity of its hardware implementation. The paper provides both benchmark results demonstrating increased efficacy and a conceptual data path/microcontroller design suitable for implementation in digital hardware. Following, it demonstrates efficient implementation by making a head-to-head comparison of field programmable gate array implementations of both the classic CGA and a member of our family of modifications. The paper concludes with a discussion of future research, including several additional extensions that we expect will further increase search efficacy without increasing implementation cost.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:8 ,  Issue: 2 )