189 research outputs found
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
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
Evolved embodied phase coordination enables robust quadruped robot locomotion
Overcoming robotics challenges in the real world requires resilient control
systems capable of handling a multitude of environments and unforeseen events.
Evolutionary optimization using simulations is a promising way to automatically
design such control systems, however, if the disparity between simulation and
the real world becomes too large, the optimization process may result in
dysfunctional real-world behaviors. In this paper, we address this challenge by
considering embodied phase coordination in the evolutionary optimization of a
quadruped robot controller based on central pattern generators. With this
method, leg phases, and indirectly also inter-leg coordination, are influenced
by sensor feedback.By comparing two very similar control systems we gain
insight into how the sensory feedback approach affects the evolved parameters
of the control system, and how the performances differs in simulation, in
transferal to the real world, and to different real-world environments. We show
that evolution enables the design of a control system with embodied phase
coordination which is more complex than previously seen approaches, and that
this system is capable of controlling a real-world multi-jointed quadruped
robot.The approach reduces the performance discrepancy between simulation and
the real world, and displays robustness towards new environments.Comment: 9 page
Spatial Evolutionary Generative Adversarial Networks
Generative adversary networks (GANs) suffer from training pathologies such as
instability and mode collapse. These pathologies mainly arise from a lack of
diversity in their adversarial interactions. Evolutionary generative
adversarial networks apply the principles of evolutionary computation to
mitigate these problems. We hybridize two of these approaches that promote
training diversity. One, E-GAN, at each batch, injects mutation diversity by
training the (replicated) generator with three independent objective functions
then selecting the resulting best performing generator for the next batch. The
other, Lipizzaner, injects population diversity by training a two-dimensional
grid of GANs with a distributed evolutionary algorithm that includes neighbor
exchanges of additional training adversaries, performance based selection and
population-based hyper-parameter tuning. We propose to combine mutation and
population approaches to diversity improvement. We contribute a superior
evolutionary GANs training method, Mustangs, that eliminates the single loss
function used across Lipizzaner's grid. Instead, each training round, a loss
function is selected with equal probability, from among the three E-GAN uses.
Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate
that Mustangs provides a statistically faster training method resulting in more
accurate networks
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation
tools for computationally expensive problems (CEPs). However, a randomly
selected algorithm may fail in solving unknown problems due to no free lunch
theorems, and it will cause more computational resource if we re-run the
algorithm or try other algorithms to get a much solution, which is more serious
in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce
the risk of choosing an inappropriate algorithm for CEPs. We propose two
portfolio frameworks for very expensive problems in which the maximal number of
fitness evaluations is only 5 times of the problem's dimension. One framework
named Par-IBSAEA runs all algorithm candidates in parallel and a more
sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound
(UCB) policy from reinforcement learning to help select the most appropriate
algorithm at each iteration. An effective reward definition is proposed for the
UCB policy. We consider three state-of-the-art individual-based SAEAs on
different problems and compare them to the portfolios built from their
instances on several benchmark problems given limited computation budgets. Our
experimental studies demonstrate that our proposed portfolio frameworks
significantly outperform any single algorithm on the set of benchmark problems
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
This paper provides a detailed investigation of using the Kullback-Leibler
(KL) Divergence as a way to compare and analyse game-levels, and hence to use
the measure as the objective function of an evolutionary algorithm to evolve
new levels. We describe the benefits of its asymmetry for level analysis and
demonstrate how (not surprisingly) the quality of the results depends on the
features used. Here we use tile-patterns of various sizes as features.
When using the measure for evolution-based level generation, we demonstrate
that the choice of variation operator is critical in order to provide an
efficient search process, and introduce a novel convolutional mutation operator
to facilitate this. We compare the results with alternative generators,
including evolving in the latent space of generative adversarial networks, and
Wave Function Collapse. The results clearly show the proposed method to provide
competitive performance, providing reasonable quality results with very fast
training and reasonably fast generation.Comment: 8 pages plus references. Proceedings of GECCO 201
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