Evolving Emergent Team Strategies in Robotic Soccer using Enhanced Cultural Algorithms

Ali, Mostafa Z.; Daoud, Mohammad; Alazrai, Rami; Reynolds, Robert G.

doi:10.1002/9781119403111.ch7

Cited by 2 publications

(3 citation statements)

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“…This can 'bootstrap' individual learning processes by exploiting previously discovered controllers for example [17]. It should also be noted that an additional form of learning -that of social learning can also be applied in the context of ER but typically requires the use of a swarm in which one robot can learn from direct interaction with another robot in the swarm [23,24]. Here we restrict our discussion to the subset of evolutionary robotics which does not rely on a swarm, i.e.…”

Section: Embodied Artificial Evolutionmentioning

confidence: 99%

“…If a new individual has access to this repository of information, it is able to learn things even when it has not experienced them directly. Cultural algorithms are prevalent in the field of swarm-robotics [23,24] in which robots learn to adapt to complex environments by learning from each other. However, here we restrict the discussion to methods in which the individual learning process of a single robot as depicted on the TOL can be influenced by external repositories of information built up over multiple generations.…”

Section: (C) Joint Optimisation With Individual and Cultural Learningmentioning

confidence: 99%

“…In contrast to cultural learning in which information discovered over evolutionary generations is stored in an external repository and can be accessed by future generations, social learning relies on direct robot–robot interactions during a single generation. That is, it assumes that there is a swarm of robots, each of which can interact with others during a generation, and can modify their behaviours according to information observed or collected via these interactions [27,28]. Thus in the case of social-learning, the learning process exploits information captured in a current generation, while cultural learning enables the learning process to exploit information gathered from multiple previous generations.…”

Section: Introductionmentioning

confidence: 99%

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Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

Hart

Goff

2021

Phil. Trans. R. Soc. B

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We survey and reflect on how learning (in the form of individual learning and/or culture) can augment evolutionary approaches to the joint optimization of the body and control of a robot. We focus on a class of applications where the goal is to evolve the body and brain of a single robot to optimize performance on a specified task. The review is grounded in a general framework for evolution which permits the interaction of artificial evolution acting on a population with individual and cultural learning mechanisms. We discuss examples of variations of the general scheme of ‘evolution plus learning’ from a broad range of robotic systems, and reflect on how the interaction of the two paradigms influences diversity, performance and rate of improvement. Finally, we suggest a number of avenues for future work as a result of the insights that arise from the review. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

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Section: Embodied Artificial Evolutionmentioning

confidence: 99%

Section: (C) Joint Optimisation With Individual and Cultural Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

Hart

Goff

2021

Phil. Trans. R. Soc. B

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HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems

Parizi

Keynia

Bardsiri

2021

Int. J. Info. Tech. Dec. Mak.

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Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.

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Evolving Emergent Team Strategies in Robotic Soccer using Enhanced Cultural Algorithms

Cited by 2 publications

References 18 publications

Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems

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