381 research outputs found
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Large-scale optimization problems that involve thousands of decision
variables have extensively arisen from various industrial areas. As a powerful
optimization tool for many real-world applications, evolutionary algorithms
(EAs) fail to solve the emerging large-scale problems both effectively and
efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA
that can not only produce high-quality solution by solving sub-problems
separately, but also highly utilizes the power of parallel computing by solving
the sub-problems simultaneously. Existing DC-based EAs that were deemed to
enjoy the same advantages of the proposed algorithm, are shown to be
practically incompatible with the parallel computing scheme, unless some
trade-offs are made by compromising the solution quality.Comment: 12 pages, 0 figure
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Given the ubiquity of non-separable optimization problems in real worlds, in
this paper we analyze and extend the large-scale version of the well-known
cooperative coevolution (CC), a divide-and-conquer optimization framework, on
non-separable functions. First, we reveal empirical reasons of why
decomposition-based methods are preferred or not in practice on some
non-separable large-scale problems, which have not been clearly pointed out in
many previous CC papers. Then, we formalize CC to a continuous game model via
simplification, but without losing its essential property. Different from
previous evolutionary game theory for CC, our new model provides a much simpler
but useful viewpoint to analyze its convergence, since only the pure Nash
equilibrium concept is needed and more general fitness landscapes can be
explicitly considered. Based on convergence analyses, we propose a hierarchical
decomposition strategy for better generalization, as for any decomposition
there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally,
we use powerful distributed computing to accelerate it under the multi-level
learning framework, which combines the fine-tuning ability from decomposition
with the invariance property of CMA-ES. Experiments on a set of
high-dimensional functions validate both its search performance and scalability
(w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores
Novelty-driven cooperative coevolution
Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents’ individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.info:eu-repo/semantics/publishedVersio
Cooperative coevolution of morphologically heterogeneous robots
Morphologically heterogeneous multirobot teams have
shown significant potential in many applications. While cooperative coevolutionary algorithms can be used for synthesising controllers for heterogeneous multirobot systems, they
have been almost exclusively applied to morphologically homogeneous systems. In this paper, we investigate if and
how cooperative coevolutionary algorithms can be used to
evolve behavioural control for a morphologically heterogeneous multirobot system. Our experiments rely on a simulated task, where a ground robot with a simple sensor-actuator
configuration must cooperate tightly with a more complex
aerial robot to find and collect items in the environment. We
first show how differences in the number and complexity of
skills each robot has to learn can impair the effectiveness of
cooperative coevolution. We then show how coevolution’s
effectiveness can be improved using incremental evolution or
novelty-driven coevolution. Despite its limitations, we show
that coevolution is a viable approach for synthesising control
for morphologically heterogeneous systems.info:eu-repo/semantics/publishedVersio
Avoiding convergence in cooperative coevolution with novelty search
Cooperative coevolution is an approach for evolving solutions composed of coadapted components. Previous research
has shown, however, that cooperative coevolutionary algorithms are biased towards stability: they tend to converge
prematurely to equilibrium states, instead of converging to
optimal or near-optimal solutions. In single-population evolutionary algorithms, novelty search has been shown capable of avoiding premature convergence to local optima —
a pathology similar to convergence to equilibrium states.
In this study, we demonstrate how novelty search can be
applied to cooperative coevolution by proposing two new
algorithms. The first algorithm promotes behavioural novelty at the team level (NS-T), while the second promotes
novelty at the individual agent level (NS-I). The proposed
algorithms are evaluated in two popular multiagent tasks:
predator-prey pursuit and keepaway soccer. An analysis
of the explored collaboration space shows that (i) fitnessbased evolution tends to quickly converge to poor equilibrium states, (ii) NS-I almost never reaches any equilibrium
state due to constant change in the individual populations,
while (iii) NS-T explores a variety of equilibrium states in
each evolutionary run and thus significantly outperforms
both fitness-based evolution and NS-I.info:eu-repo/semantics/acceptedVersio
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search
Evolutionary algorithms (EAs) have been successfully applied to optimize the
policies for Reinforcement Learning (RL) tasks due to their exploration
ability. The recently proposed Negatively Correlated Search (NCS) provides a
distinct parallel exploration search behavior and is expected to facilitate RL
more effectively. Considering that the commonly adopted neural policies usually
involves millions of parameters to be optimized, the direct application of NCS
to RL may face a great challenge of the large-scale search space. To address
this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC)
framework to scale-up NCS while largely preserving its parallel exploration
search behavior. The issue of traditional CC that can deteriorate NCS is also
discussed. Empirical studies on 10 popular Atari games show that the proposed
method can significantly outperform three state-of-the-art deep RL methods with
50% less computational time by effectively exploring a 1.7 million-dimensional
search space
An exploration of evolutionary computation applied to frequency modulation audio synthesis parameter optimisation
With the ever-increasing complexity of sound synthesisers, there is a growing demand for automated parameter estimation and sound space navigation techniques. This thesis explores the potential for evolutionary computation to automatically map known sound qualities onto the parameters of frequency modulation synthesis. Within this exploration are original contributions in the domain of synthesis parameter estimation and, within the developed system, evolutionary computation, in the form of the evolutionary algorithms that drive the underlying optimisation process. Based upon the requirement for the parameter estimation system to deliver multiple search space solutions, existing evolutionary algorithmic architectures are augmented to enable niching, while maintaining the strengths of the original algorithms. Two novel evolutionary algorithms are proposed in which cluster analysis is used to identify and maintain species within the evolving populations. A conventional evolution strategy and cooperative coevolution strategy are defined, with cluster-orientated operators that enable the simultaneous optimisation of multiple search space solutions at distinct optima. A test methodology is developed that enables components of the synthesis matching problem to be identified and isolated, enabling the performance of different optimisation techniques to be compared quantitatively. A system is consequently developed that evolves sound matches using conventional frequency modulation synthesis models, and the effectiveness of different evolutionary algorithms is assessed and compared in application to both static and timevarying sound matching problems. Performance of the system is then evaluated by interview with expert listeners. The thesis is closed with a reflection on the algorithms and systems which have been developed, discussing possibilities for the future of automated synthesis parameter estimation techniques, and how they might be employed
- …