262 research outputs found
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach
The definition of a concise and effective testbed for Genetic Programming
(GP) is a recurrent matter in the research community. This paper takes a new
step in this direction, proposing a different approach to measure the quality
of the symbolic regression benchmarks quantitatively. The proposed approach is
based on meta-learning and uses a set of dataset meta-features---such as the
number of examples or output skewness---to describe the datasets. Our idea is
to correlate these meta-features with the errors obtained by a GP method. These
meta-features define a space of benchmarks that should, ideally, have datasets
(points) covering different regions of the space. An initial analysis of 63
datasets showed that current benchmarks are concentrated in a small region of
this benchmark space. We also found out that number of instances and output
skewness are the most relevant meta-features to GP output error. Both
conclusions can help define which datasets should compose an effective testbed
for symbolic regression methods.Comment: 8 pages, 3 Figures, Proceedings of Genetic and Evolutionary
Computation Conference Companion, Kyoto, Japa
Interoceptive robustness through environment-mediated morphological development
Typically, AI researchers and roboticists try to realize intelligent behavior
in machines by tuning parameters of a predefined structure (body plan and/or
neural network architecture) using evolutionary or learning algorithms. Another
but not unrelated longstanding property of these systems is their brittleness
to slight aberrations, as highlighted by the growing deep learning literature
on adversarial examples. Here we show robustness can be achieved by evolving
the geometry of soft robots, their control systems, and how their material
properties develop in response to one particular interoceptive stimulus
(engineering stress) during their lifetimes. By doing so we realized robots
that were equally fit but more robust to extreme material defects (such as
might occur during fabrication or by damage thereafter) than robots that did
not develop during their lifetimes, or developed in response to a different
interoceptive stimulus (pressure). This suggests that the interplay between
changes in the containing systems of agents (body plan and/or neural
architecture) at different temporal scales (evolutionary and developmental)
along different modalities (geometry, material properties, synaptic weights)
and in response to different signals (interoceptive and external perception)
all dictate those agents' abilities to evolve or learn capable and robust
strategies
ES Is More Than Just a Traditional Finite-Difference Approximator
An evolution strategy (ES) variant based on a simplification of a natural
evolution strategy recently attracted attention because it performs
surprisingly well in challenging deep reinforcement learning domains. It
searches for neural network parameters by generating perturbations to the
current set of parameters, checking their performance, and moving in the
aggregate direction of higher reward. Because it resembles a traditional
finite-difference approximation of the reward gradient, it can naturally be
confused with one. However, this ES optimizes for a different gradient than
just reward: It optimizes for the average reward of the entire population,
thereby seeking parameters that are robust to perturbation. This difference can
channel ES into distinct areas of the search space relative to gradient
descent, and also consequently to networks with distinct properties. This
unique robustness-seeking property, and its consequences for optimization, are
demonstrated in several domains. They include humanoid locomotion, where
networks from policy gradient-based reinforcement learning are significantly
less robust to parameter perturbation than ES-based policies solving the same
task. While the implications of such robustness and robustness-seeking remain
open to further study, this work's main contribution is to highlight such
differences and their potential importance
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of
features. When artificial neural networks (ANNs) are used for modeling these
tasks, the network architectures tend to be large. Neuroevolution is an
effective approach for optimizing ANNs; however, there are two bottlenecks that
make their application challenging in case of high-dimensional networks using
direct encoding. First, classic evolutionary algorithms tend not to scale well
for searching large parameter spaces; second, the network evaluation over a
large number of training instances is in general time-consuming. In this work,
we propose an approach called the Limited Evaluation Cooperative
Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize
high-dimensional ANNs.
The proposed method aims to optimize the pre-synaptic weights of each
post-synaptic neuron in different subpopulations using a Cooperative
Co-evolutionary Differential Evolution algorithm, and employs a limited
evaluation scheme where fitness evaluation is performed on a relatively small
number of training instances based on fitness inheritance. We test LECCDE on
three datasets with various sizes, and our results show that cooperative
co-evolution significantly improves the test error comparing to standard
Differential Evolution, while the limited evaluation scheme facilitates a
significant reduction in computing time
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
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
While neuroevolution (evolving neural networks) has a successful track record
across a variety of domains from reinforcement learning to artificial life, it
is rarely applied to large, deep neural networks. A central reason is that
while random mutation generally works in low dimensions, a random perturbation
of thousands or millions of weights is likely to break existing functionality,
providing no learning signal even if some individual weight changes were
beneficial. This paper proposes a solution by introducing a family of safe
mutation (SM) operators that aim within the mutation operator itself to find a
degree of change that does not alter network behavior too much, but still
facilitates exploration. Importantly, these SM operators do not require any
additional interactions with the environment. The most effective SM variant
capitalizes on the intriguing opportunity to scale the degree of mutation of
each individual weight according to the sensitivity of the network's outputs to
that weight, which requires computing the gradient of outputs with respect to
the weights (instead of the gradient of error, as in conventional deep
learning). This safe mutation through gradients (SM-G) operator dramatically
increases the ability of a simple genetic algorithm-based neuroevolution method
to find solutions in high-dimensional domains that require deep and/or
recurrent neural networks (which tend to be particularly brittle to mutation),
including domains that require processing raw pixels. By improving our ability
to evolve deep neural networks, this new safer approach to mutation expands the
scope of domains amenable to neuroevolution
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent
premature convergence towards a single local optimum. In order to maintain
diversity throughout the process of evolution, various means exist in
literature. We analyze approaches to diversity that (a) have an explicit and
quantifiable influence on fitness at the individual level and (b) require no
(or very little) additional domain knowledge such as domain-specific distance
functions. We also introduce the concept of genealogical diversity in a broader
study. We show that employing these approaches can help evolutionary algorithms
for global optimization in many cases.Comment: GECCO '18: Genetic and Evolutionary Computation Conference, 2018,
Kyoto, Japa
Combating catastrophic forgetting with developmental compression
Generally intelligent agents exhibit successful behavior across problems in
several settings. Endemic in approaches to realize such intelligence in
machines is catastrophic forgetting: sequential learning corrupts knowledge
obtained earlier in the sequence, or tasks antagonistically compete for system
resources. Methods for obviating catastrophic forgetting have sought to
identify and preserve features of the system necessary to solve one problem
when learning to solve another, or to enforce modularity such that minimally
overlapping sub-functions contain task specific knowledge. While successful,
both approaches scale poorly because they require larger architectures as the
number of training instances grows, causing different parts of the system to
specialize for separate subsets of the data. Here we present a method for
addressing catastrophic forgetting called developmental compression. It
exploits the mild impacts of developmental mutations to lessen adverse changes
to previously-evolved capabilities and `compresses' specialized neural networks
into a generalized one. In the absence of domain knowledge, developmental
compression produces systems that avoid overt specialization, alleviating the
need to engineer a bespoke system for every task permutation and suggesting
better scalability than existing approaches. We validate this method on a robot
control problem and hope to extend this approach to other machine learning
domains in the future
Enhancing partition crossover with articulation points analysis
Partition Crossover is a recombination operator for pseudo-Boolean optimization with the ability to explore an exponential number of solutions in linear or square time. It decomposes the objective function as a sum of subfunctions, each one depending on a different set of variables. The decomposition makes it possible to select the best parent for each subfunction independently, and the operator provides the best out of solutions, where is the number of subfunctions in the decomposition. These subfunctions are defined over the connected components of the recombination graph: a subgraph of the objective function variable interaction graph containing only the differing variables in the two parents. In this paper, we advance further and propose a new way to increase the number of linearly independent subfunctions by analyzing the articulation points of the recombination graph. These points correspond to variables that, once flipped, increase the number of connected components. The presence of a connected component with an articulation point increases the number of explored solutions by a factor of, at least, 4. We evaluate the new operator using Iterated Local Search combined with Partition Crossover to solve NK Landscapes and MAX-SAT.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech.
Funding was provided by the Fulbright program, the Spanish Ministry of Education, Culture and Sport (CAS12/00274), the Spanish Ministry of Economy and Competitiveness and FEDER (TIN2014-57341-R and TIN2017-88213-R), the Air Force Office of Scientific Research, (FA9550-11-1-0088), the Leverhulme Trust (RPG-2015-395), the FAPESP (2015/06462-1) and CNPq (304400/2014-9)
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