1,649 research outputs found
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
This paper proposes a self-adaptation mechanism to manage the resources
allocated to the different species comprising a cooperative coevolutionary
algorithm. The proposed approach relies on a dynamic extension to the
well-known multi-armed bandit framework. At each iteration, the dynamic
multi-armed bandit makes a decision on which species to evolve for a
generation, using the history of progress made by the different species to
guide the decisions. We show experimentally, on a benchmark and a real-world
problem, that evolving the different populations at different paces allows not
only to identify solutions more rapidly, but also improves the capacity of
cooperative coevolution to solve more complex problems.Comment: Accepted at GECCO 201
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Evolutionary improvement of programs
Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues
Ms Pac-Man versus Ghost Team CEC 2011 competition
Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE
Evolutionary Reinforcement Learning: A Survey
Reinforcement learning (RL) is a machine learning approach that trains agents
to maximize cumulative rewards through interactions with environments. The
integration of RL with deep learning has recently resulted in impressive
achievements in a wide range of challenging tasks, including board games,
arcade games, and robot control. Despite these successes, there remain several
crucial challenges, including brittle convergence properties caused by
sensitive hyperparameters, difficulties in temporal credit assignment with long
time horizons and sparse rewards, a lack of diverse exploration, especially in
continuous search space scenarios, difficulties in credit assignment in
multi-agent reinforcement learning, and conflicting objectives for rewards.
Evolutionary computation (EC), which maintains a population of learning agents,
has demonstrated promising performance in addressing these limitations. This
article presents a comprehensive survey of state-of-the-art methods for
integrating EC into RL, referred to as evolutionary reinforcement learning
(EvoRL). We categorize EvoRL methods according to key research fields in RL,
including hyperparameter optimization, policy search, exploration, reward
shaping, meta-RL, and multi-objective RL. We then discuss future research
directions in terms of efficient methods, benchmarks, and scalable platforms.
This survey serves as a resource for researchers and practitioners interested
in the field of EvoRL, highlighting the important challenges and opportunities
for future research. With the help of this survey, researchers and
practitioners can develop more efficient methods and tailored benchmarks for
EvoRL, further advancing this promising cross-disciplinary research field
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
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