12,336 research outputs found
A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation
In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits
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
Evolutionary Algorithms for Reinforcement Learning
There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications
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
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
Hanabi is a cooperative game that brings the problem of modeling other
players to the forefront. In this game, coordinated groups of players can
leverage pre-established conventions to great effect, but playing in an ad-hoc
setting requires agents to adapt to its partner's strategies with no previous
coordination. Evaluating an agent in this setting requires a diverse population
of potential partners, but so far, the behavioral diversity of agents has not
been considered in a systematic way. This paper proposes Quality Diversity
algorithms as a promising class of algorithms to generate diverse populations
for this purpose, and generates a population of diverse Hanabi agents using
MAP-Elites. We also postulate that agents can benefit from a diverse population
during training and implement a simple "meta-strategy" for adapting to an
agent's perceived behavioral niche. We show this meta-strategy can work better
than generalist strategies even outside the population it was trained with if
its partner's behavioral niche can be correctly inferred, but in practice a
partner's behavior depends and interferes with the meta-agent's own behavior,
suggesting an avenue for future research in characterizing another agent's
behavior during gameplay.Comment: arXiv admin note: text overlap with arXiv:1907.0384
A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Despite their claimed biological plausibility, most self organizing networks
have strict topological constraints and consequently they cannot take into
account a wide range of external stimuli. Furthermore their evolution is
conditioned by deterministic laws which often are not correlated with the
structural parameters and the global status of the network, as it should happen
in a real biological system. In nature the environmental inputs are noise
affected and fuzzy. Which thing sets the problem to investigate the possibility
of emergent behaviour in a not strictly constrained net and subjected to
different inputs. It is here presented a new model of Evolutionary Neural Gas
(ENG) with any topological constraints, trained by probabilistic laws depending
on the local distortion errors and the network dimension. The network is
considered as a population of nodes that coexist in an ecosystem sharing local
and global resources. Those particular features allow the network to quickly
adapt to the environment, according to its dimensions. The ENG model analysis
shows that the net evolves as a scale-free graph, and justifies in a deeply
physical sense- the term gas here used.Comment: 16 pages, 8 figure
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