Benefits of lamarckian evolution for morphologically evolving robots

Jelisavcic, Milan; Kiesel, Rafael; Glette, Kyrre; Haasdijk, Evert; Eiben, A. E.

doi:10.1145/3067695.3076046

Cited by 2 publications

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“…Although the idea seems straightforward, until recently there has been hardly any research into the effects of Lamarckian set-ups combining morphological evolution and lifetime learning of robot controllers. Jelisavcic et al ( 2017c ) report preliminary findings that indicate that such a Lamarckian set-up can improve performance. A more in-depth follow-up investigation reported a strong correlation between the advantage of inheriting parental controllers and the amount of retained parental controller structure (Jelisavcic et al, 2017b ).…”

Section: Introductionmentioning

confidence: 99%

Lamarckian Evolution of Simulated Modular Robots

et al. 2019

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We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after 'birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies.

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Section: Introductionmentioning

confidence: 99%

Lamarckian Evolution of Simulated Modular Robots

et al. 2019

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Learning and Evolution: Factors Influencing an Effective Combination

Pagliuca

2024

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(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2) Methods: In this paper, the author investigates whether combining learning and evolution permits finding better solutions than those discovered by evolution alone. In further detail, the author presents a series of empirical studies that highlight some specific conditions determining the success of such combination. Results are obtained in five qualitatively different domains: (i) the 5-bit parity task, (ii) the double-pole balancing problem, (iii) the Rastrigin, Rosenbrock and Sphere optimization functions, (iv) a robot foraging task and (v) a social foraging problem. Moreover, the first three tasks represent benchmark problems in the field of evolutionary computation. (3) Results and discussion: The outcomes indicate that the effect of learning on evolution depends on the nature of the problem. Specifically, when the problem implies limited or absent agent–environment conditions, learning is beneficial for evolution, especially with the introduction of noise during the learning and selection processes. Conversely, when agents are embodied and actively interact with the environment, learning does not provide advantages, and the addition of noise is detrimental. Finally, the absence of stochasticity in the experienced conditions is paramount for the effectiveness of the combination. Furthermore, the length of the learning process must be fine-tuned based on the considered task.

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Benefits of lamarckian evolution for morphologically evolving robots

Cited by 2 publications

References 3 publications

Lamarckian Evolution of Simulated Modular Robots

Lamarckian Evolution of Simulated Modular Robots

Learning and Evolution: Factors Influencing an Effective Combination

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