Open AccessProceedings Article
An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach
Julian F. Miller
- pp 1135-1142
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TLDR
The efficacy of the PH suggests that boolean function learning may not be an appropriate problem for testing the effectiveness of GP and EP, and that extremely low populations are most effective.Abstract:
A new form of Genetic Programming (GP) called Cartesian Genetic Programming (CGP) is proposed in which programs are represented by linear integer chromosomes in the form of connections and functionalities of a rectangular array of primitive functions. The effectiveness of this approach is investigated for boolean even-parity functions (3,4,5), and the 2-bit multiplier. The minimum number of evaluations required to give a 0.99 probability of evolving a target function is used to measure the efficiency of the new approach. It is found that extremely low populations are most effective. A simple probabilistic hillclimber (PH) is devised which proves to be even more effective. For these boolean functions either method appears to be much more efficient than the GP and Evolutionary Programming (EP) methods reported. The efficacy of the PH suggests that boolean function learning may not be an appropriate problem for testing the effectiveness of GP and EP.read more
Citations
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Book
A Field Guide to Genetic Programming
TL;DR: A unique overview of this exciting technique is written by three of the most active scientists in GP, which starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination until high-fitness solutions emerge.
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Cartesian Genetic Programming
Julian F. Miller,P. Thomson +1 more
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Principles in the Evolutionary Design of Digital Circuits—Part II
TL;DR: It is argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design, which explain how to build systems which are too large to evolve.
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TL;DR: The genotype–phenotype mapping used in CGP is one of its defining characteristics and its types are decided by the user and are listed in a function look-up table.
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Redundancy and computational efficiency in Cartesian genetic programming
TL;DR: The results presented demonstrate the role of mutation and genotype length in the evolvability of the graph-based Cartesian genetic programming system and find that the most evolvable representations occur when the genotype is extremely large and in which over 95% of the genes are inactive.
References
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