3,667 research outputs found
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Surrogate-assistance approaches have long been used in computationally
expensive domains to improve the data-efficiency of optimization algorithms.
Neuroevolution, however, has so far resisted the application of these
techniques because it requires the surrogate model to make fitness predictions
based on variable topologies, instead of a vector of parameters. Our main
insight is that we can sidestep this problem by using kernel-based surrogate
models, which require only the definition of a distance measure between
individuals. Our second insight is that the well-established Neuroevolution of
Augmenting Topologies (NEAT) algorithm provides a computationally efficient
distance measure between dissimilar networks in the form of "compatibility
distance", initially designed to maintain topological diversity. Combining
these two ideas, we introduce a surrogate-assisted neuroevolution algorithm
that combines NEAT and a surrogate model built using a compatibility distance
kernel. We demonstrate the data-efficiency of this new algorithm on the low
dimensional cart-pole swing-up problem, as well as the higher dimensional
half-cheetah running task. In both tasks the surrogate-assisted variant
achieves the same or better results with several times fewer function
evaluations as the original NEAT.Comment: In GECCO 201
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the Banking Sector
Artificial neural networks have been universally acknowledged for their ability on constructing forecasting and classifying systems. Among their desirable features, it has always been the interpretation of their structure, aiming to provide further knowledge for the domain experts. A number of methodologies have been developed for this reason. One such paradigm is the neural logic networks concept. Neural logic networks have been especially designed in order to enable the interpretation of their structure into a number of simple logical rules and they can be seen as a network representation of a logical rule base. Although powerful by their definition in this context, neural logic networks have performed poorly when used in approaches that required training from data. Standard training methods, such as the back-propagation, require the network’s synapse weight altering, which destroys the network’s interpretability. The methodology in this paper overcomes these problems and proposes an architecture-altering technique, which enables the production of highly antagonistic solutions while preserving any weight-related information. The implementation involves genetic programming using a grammar-guided training approach, in order to provide arbitrarily large and connected neural logic networks. The methodology is tested in a problem from the banking sector with encouraging results
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search
Evolutionary algorithms (EAs) have gained attention recently due to their
success in neural architecture search (NAS). However, whereas traditional EAs
draw much power from crossover operations, most evolutionary NAS methods deploy
only mutation operators. The main reason is the permutation problem: The
mapping between genotype and phenotype in traditional graph representations is
many-to-one, leading to a disruptive effect of standard crossover. This work
conducts the first theoretical analysis of the behaviors of crossover and
mutation in the NAS context, and proposes a new crossover operator based on the
shortest edit path (SEP) in graph space. The SEP crossover is shown to overcome
the permutation problem, and as a result, offspring generated by the SEP
crossover is theoretically proved to have a better expected improvement in
terms of graph edit distance to global optimum, compared to mutation and
standard crossover. Experiments further show that the SEP crossover
significantly outperforms mutation and standard crossover on three
state-of-the-art NAS benchmarks. The SEP crossover therefore allows taking full
advantage of evolution in NAS, and potentially other similar design problems as
well.Comment: 17 pages, 6 figure
Modularity based linkage model for neuroevolution
Crossover between neural networks is considered disruptive due to the strong
functional dependency between connection weights. We propose a modularity-based
linkage model at the weight level to preserve functionally dependent
communities (building blocks) in neural networks during mixing. A proximity
matrix is built by estimating the dependency between weights, then a community
detection algorithm maximizing modularity is run on the graph described by such
matrix. The resulting communities/groups of parameters are considered to be
mutually independent and used as crossover masks in an optimal mixing EA. A
variant is tested with an operator that neutralizes the permutation problem of
neural networks to a degree. Experiments were performed on 8 and 10-bit parity
problems as the intrinsic hierarchical nature of the dependencies in these
problems are challenging to learn. The results show that our algorithm finds
better, more functionally dependent linkage which leads to more successful
crossover and better performance
An empirical evaluation of evolutionary controller design methods for collective gathering task
This research aims to evaluate the performance of evolutionary controller design methods for developing a collective behaviour for a team of robots. The methods tested in this research are NEAT which is capable of finding minimal solution quickly, and SANE which maintains high genetic diversity through neuron level evolution. The task chosen for these methods was a collective gathering task which required a team of robots to cooperate in finding and retrieving item of interest. Our results showed that NEAT consistently produced better controllers compared to SANE
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