In autoconstructive evolutionary algorithms, individuals implement not only candidate solutions to specified computational problems, but also their own methods for variation of offspring. This makes it possible for the variation methods to themselves evolve, which could, in principle, produce a system with an enhanced capacity for adaptation and superior problem solving power. Prior work on autoconsruction has explored a range of system designs and their evolutionary dynamics, but it has not solved hard problems. Here we describe a new approach that can indeed solve at least some hard problems. We present the key components of this approach, including the use of linear genomes for hierarchically structured programs, a diversity-maintaining parent selection algorithm, and the enforcement of diversification constraints on offspring. We describe a software synthesis benchmark problem that our new approach can solve, and we present visualizations of data from single successful runs of autoconstructive vs. non-autoconstructive systems on this problem. While anecdotal, the data suggests that variation methods, and therefore significant aspects of the evolutionary process, evolve over the course of the autoconstructive runs.
|Original language||English (US)|
|Title of host publication||GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|State||Published - Jul 20 2016|
|Event||2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States|
Duration: Jul 20 2016 → Jul 24 2016
|Name||GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference|
|Other||2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion|
|Period||7/20/16 → 7/24/16|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation under Grants No. 1129139 and 1331283.
© 2016 ACM.
Copyright 2017 Elsevier B.V., All rights reserved.
- Autoconstructive evolution
- Genetic programming