482 research outputs found
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and
Practice 2017 worksho
Semantic variation operators for multidimensional genetic programming
Multidimensional genetic programming represents candidate solutions as sets
of programs, and thereby provides an interesting framework for exploiting
building block identification. Towards this goal, we investigate the use of
machine learning as a way to bias which components of programs are promoted,
and propose two semantic operators to choose where useful building blocks are
placed during crossover. A forward stagewise crossover operator we propose
leads to significant improvements on a set of regression problems, and produces
state-of-the-art results in a large benchmark study. We discuss this
architecture and others in terms of their propensity for allowing heuristic
search to utilize information during the evolutionary process. Finally, we look
at the collinearity and complexity of the data representations that result from
these architectures, with a view towards disentangling factors of variation in
application.Comment: 9 pages, 8 figures, GECCO 201
A rewriting grammar for heat exchanger network structure evolution with stream splitting
The design of cost optimal heat exchanger networks is a difficult optimisation problem due
both to the nonlinear models required and also the combinatorial size of the search space.
When stream splitting is considered, the combinatorial aspects make the problem even harder.
This paper describes the implementation of a two level evolutionary algorithm based on a
string rewriting grammar for the evolution of the heat exchanger network structure. A biological analogue of genotypes and phenotypes is used to describe structures and specific solutions respectively. The top level algorithm evolves structures while the lower level optimises specific
structures. The result is a hybrid optimisation procedure which can identify the best structures including stream splitting. Case studies from the literature are presented to demonstrate the capabilities of the novel procedure
Comparing and Combining Lexicase Selection and Novelty Search
Lexicase selection and novelty search, two parent selection methods used in
evolutionary computation, emphasize exploring widely in the search space more
than traditional methods such as tournament selection. However, lexicase
selection is not explicitly driven to select for novelty in the population, and
novelty search suffers from lack of direction toward a goal, especially in
unconstrained, highly-dimensional spaces. We combine the strengths of lexicase
selection and novelty search by creating a novelty score for each test case,
and adding those novelty scores to the normal error values used in lexicase
selection. We use this new novelty-lexicase selection to solve automatic
program synthesis problems, and find it significantly outperforms both novelty
search and lexicase selection. Additionally, we find that novelty search has
very little success in the problem domain of program synthesis. We explore the
effects of each of these methods on population diversity and long-term problem
solving performance, and give evidence to support the hypothesis that
novelty-lexicase selection resists converging to local optima better than
lexicase selection
Local Search is Underused in Genetic Programming
Trujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied extensively, with many works proposing bloat control methods. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while also reducing program size. Given these results, a question arises: Why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.authorsversionpublishe
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