Novelty Search in Competitive Coevolution

Gomes, Jorge; Mariano, Pedro; Christensen, Anders Lyhne

doi:10.1007/978-3-319-10762-2_23

Cited by 7 publications

(4 citation statements)

References 16 publications

(28 reference statements)

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“…This lack of effectiveness contrasts with previous works where PMCNS was successfully used [8,10,11]. Our results suggest that PMCNS's low performance is due to the high degree of deception in most mazes.…”

Section: Combination Of Novelty and Fitnesscontrasting

confidence: 85%

Devising Effective Novelty Search Algorithms

Gomes

Mariano

Christensen

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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102

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Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty instead of pursuing a static objective. Along with a large number of successful applications, many different variants of novelty search have been proposed. It is still unclear, however, how some key parameters and algorithmic components influence the evolutionary dynamics and performance of novelty search. In this paper, we conduct a comprehensive empirical study focused on novelty search's algorithmic components. We study the k parameter -the number of nearest neighbours used in the computation of novelty scores; the use and function of an archive; how to combine novelty search with fitness-based evolution; and how to configure the mutation rate of the underlying evolutionary algorithm. Our study is conducted in a simulated maze navigation task. Our results show that the configuration of novelty search can have a significant impact on performance and behaviour space exploration. We conclude with a number of guidelines for the implementation and configuration of novelty search, which should help future practitioners to apply novelty search more effectively.

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Section: Combination Of Novelty and Fitnesscontrasting

confidence: 85%

Devising Effective Novelty Search Algorithms

Gomes

Mariano

Christensen

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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102

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“…Related works such as Gomes' competitive [37] and cooperative [38] evolution papers have a strong adherence to such definitions, but this paper focuses on the evolution of a single predator agent, so the prey agents only loosely follow such definitions. The structure of a simple intelligent agent can be see in Figure 2.7.…”

Section: Embodied Agentsmentioning

confidence: 99%

“…Coevolution. Coevolution can be implemented as competitive [37] or cooperative [38]. As competitive coevolution, the prey would be given the ability to evolve as well.…”

Section: Time-based Scoringmentioning

confidence: 99%

Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains

Cowan,

Ross

2024

Lecture Notes in Computer Science

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Evolutionary algorithms have a tendency to overuse and exploit particular behaviours in their search for optimality, even across separate runs. The resulting set of monotonous solutions caused by this tendency is a problem in many applications. This research explores different strategies designed to encourage an interesting set of diverse behaviours while still maintaining an appreciable level of efficacy. Embodied agents are situated within an open plane and play against each other in various pursuit game scenarios. The pursuit games consist of a single predator agent and twenty prey agents, with the goal always requiring the predator to catch as many prey as possible before the time limit is reached. The predator's controller is evolved through genetic programming while the preys' controllers are hand-crafted. The fitness of a solution is first calculated in a traditional manner. Inspired by Lehman and Stanley's novelty search strategy, the fitness is then combined with the diversity of the solution to produce the final fitness score.The original fitness score is determined by the number of captured prey, and the diversity score is determined through the combination of four behaviour measurements. Among many promising results, a particular diversity-based evaluation strategy and weighting combination was found to provide solutions that exhibit an excellent balance between diversity and efficacy. The results were analyzed quantitatively and qualitatively, showing the emergence of diverse and effective behaviours.Writing a thesis during a pandemic is no easy task. As the lines between work, school, and life became blurred, focusing on what needed to be done became increasingly difficult.Fortunately, I had the assistance of many wonderful people throughout my research. It's thanks to all of you that I was able to complete this thesis. First, I'd like to thank my supervisor, Dr. Brian J. Ross, for recommending that I consider enrolling as a Master of Science. I am forever grateful for him taking me under his wing, teaching me what he knows about the concepts used throughout this research, and providing me with continual support and assistance. I'd like to thank Illya Bakurov, Michael Gircys, and Doug Ord for their contributions toward the implementation of my systems. Their experience in genetic programming, multi-objectivization, and game-development saved me plenty of time during the development stages of this research. I'd like to thank my friends, Mitchell Clark and Jordan Maslen. The three of us met within our first month as post-secondary students and we road out the entire journey together. I have absolutely no idea where I would be today had I not met you two, so, for the many all-nighters and crunch sessions, thank you. I'd like to express heartfelt gratitude to my significant other, the most incredible woman I have ever met: Sara Testani. Your endless support and encouragement has helped me in so many ways throughout this thesis, and you've helped me find myself throughout this incredibly stressful pandem...

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“…We purposefully are not too stringent in setting our goal -being able to infect a single bacterium (or being infected by a single phage) suffices, and there is no need to be optimal. This is an instance of Minimal Criterion Coevolution [57] where two interacting populations are maintained, with entities only having to meet some minimal functionality criterion to be included. Random variations introduced after each cycle ensures that the population's diversity is maintained while the criterion ensures they remain functional.…”

Section: Realizing Open-endedness For Biological Designmentioning

confidence: 99%

Open-endedness in synthetic biology: a route to continual innovation for biological design

Stock¹,

Gorochowski²

2023

Preprint

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Design in synthetic biology is typically goal-oriented: aiming to repurpose and optimize existing biological functions for new contexts, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances over the past two decades, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design that aims to continuously generate innovative solutions capable of overcoming such hurdles. The fundamental principle we put forward is that the novelty of designed biology, be it a protein, genetic construct or organism, is at least as important as how well it fulfils its goal. Designing with novelty in mind, rather than a sole focus on optimization towards a single ``best'' design, may allow us to move beyond the diminishing returns we see in performance for most engineered biology to date. Research from the artificial life and evolutionary computing communities has demonstrated that embracing novelty can allow for the automatic generation of innovative and surprising solutions to challenging problems that move us beyond local optima. Synthetic biology now offers the ideal playground to test these ideas in living systems and explore more creative approaches to biological design.

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Novelty Search in Competitive Coevolution

Cited by 7 publications

References 16 publications

Devising Effective Novelty Search Algorithms

Devising Effective Novelty Search Algorithms

Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains

Open-endedness in synthetic biology: a route to continual innovation for biological design

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