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Local evolvability of statistically neutral GasNet robot controllers

BIOSYSTEMS 2003年第2-3期69卷 223-243页

作者： Smith, T Husbands, P O'Shea, M Univ Sussex CCNR Brighton E Sussex England Univ Sussex Sch Biol Sci Brighton E Sussex England Univ Sussex Sch Cognit & Comp Sci Brighton E Sussex England

In this paper we introduce and apply the concept of local evolvability to investigate the behaviour of populations during evolutionary search. We focus on the evolution of GasNet neural network controllers for a robotic visual discrimination problem, showing that the evolutionary process undergoes long neutral fitness epochs. We show that the local evolvability properties of the search space surrounding a group of statistically neutral solutions do vary across the course of an evolutionary run, especially during periods of population takeover. However, once takeover is complete there is no evidence for further increase in local evolvability across fitness epochs. We also see no evidence for the neutral evolution of increased solution robustness, but show that this may be due to the ability of evolutionary algorithms to focus search on volumes of the fitness landscape with above average robustness. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

关键词： local evolvability neutral evolution fitness landscape evolutionary algorithm evolutionary robotics solution robustness

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Evolving locomotion for a 12-DOF quadruped robot in simulated environments

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BIOSYSTEMS 2013年第2期112卷 102-106页

作者： Klaus, Gordon Glette, Kyrre Hoyin, Mats Univ Oslo Dept Informat N-0373 Oslo Norway

We demonstrate the power of evolutionary robotics (ER) by comparing to a more traditional approach its performance and cost on the task of simulated robot locomotion. A novel quadruped robot is introduced, the legs of which - each having three non-coplanar degrees of freedom - are very maneuverable. Using a simplistic control architecture and a physics simulation of the robot, gaits are designed both by hand and using a highly parallel evolutionary algorithm (EA). It is found that the EA produces, in a small fraction of the time that takes to design by hand, gaits that travel at two to four times the speed of the hand-designed one. The flexibility of this approach is demonstrated by applying it across a range of differently configured simulators. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

关键词： evolutionary robotics Simulation Locomotion

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The memorization of in-line sensorimotor invariants: toward behavioral ontogeny and enactive agents

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ARTIFICIAL LIFE AND robotics 2014年第2期19卷 127-135页

作者： De Loor, Pierre Manac'h, Kristen Chevaillier, Pierre ENIB UEB Lab STICC F-29200 Brest France

This paper presents a behavioral ontogeny for artificial agents based on the interactive memorization of sensorimotor invariants. The agents are controlled by continuous timed recurrent neural networks (CTRNNs) which bind their sensors and motors within a dynamic system. The behavioral ontogenesis is based on a phylogenetic approach: memorization occurs during the agent's lifetime and an evolutionary algorithm discovers CTRNN parameters. This shows that sensorimotor invariants can be durably modified through interaction with a guiding agent. After this phase has finished, agents are able to adopt new sensorimotor invariants relative to the environment with no further guidance. We obtained these kinds of behaviors for CTRNNs with 3-6 units, and this paper examines the functioning of those CTRNNs. For instance, they are able to internally simulate guidance when it is externally absent, in line with theories of simulation in neuroscience and the enactive field of cognitive science.

关键词： Ontogeny evolutionary robotics Embodied agent Sensorimotor invariants CTRNN Memory Enaction

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Biologically Inspired Computing

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COMPUTER 2009年第4期42卷 95-98页

作者： Bongard, Josh Univ Vermont Dept Comp Sci Burlington VT 05405 USA

evolutionary algorithms and robotics hold great promise as integrated design and modeling tools.

关键词： Bio Inspired Computing evolutionary robotics Artificial Intelligence Intelligent Systems

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Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity

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ARTIFICIAL LIFE 2015年第4期21卷 464-480页

作者： Hamann, Heiko Univ Paderborn Paderborn Germany

Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance, which often includes task-specific features and hence a priori knowledge. Alternative methods, which do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life, such as in artificial ecologies (AEs). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. In a simple case study we investigate the practical implications of these findings and point to options for transferring the idea of self-organizing SPTD in AEs to the domain of ER.

关键词： evolutionary robotics speciation selection

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Time efficiency in optimization with a bayesian-evolutionary algorithm

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SWARM AND evolutionary COMPUTATION 2022年 69卷 100970-100970页

作者： Lan, Gongjin Tomczak, Jakub M. Roijers, Diederik M. Eiben, A. E. Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Guangdong Peoples R China Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands Vrije Univ Brussel AI Lab Brussels Belgium HU Univ Appl Sci Utrecht Inst ICT Utrecht Netherlands

Not all generate-and-test search algorithms are created equal. Bayesian Optimization (BO) invests a lot of computation time to generate the candidate solution that best balances the predicted value and the uncertainty given all previous data, taking increasingly more time as the number of evaluations performed grows. evolutionary Algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time. Both BO and EA community typically assess their performance as a function of the number of evaluations, i.e., data efficiency. However, this is unfair once we start to compare the efficiency of these classes of algorithms, as the overhead times to generate candidate solutions are significantly different . We suggest to measure the efficiency of generate-and-test search algorithms as the expected gain in the objective value per unit of computation time spent, i.e., time efficiency. To the time-efficient search algorithm, we therefore propose a new algorithm, a combination of BO and an EA, BEA for short, that starts with BO, then transfers knowledge to an EA, and subsequently runs the EA. We compare the BEA with BO, the EA, Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results show that BEA outperforms BO, the EA, DE and PSO in terms of time efficiency, and ultimately leads to better performance on well-known benchmark objective functions with many local optima. Moreover, we test BEA, BO, and the EA on nine test cases of robot learning problems and here again we find that BEA outperforms the other algorithms.

关键词： Optimization algorithms Time efficiency Bayesian optimization evolutionary algorithm Differential evolution Particle swarm optimization evolutionary robotics

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Distributed evolutionary learning control for mobile robot navigation based on virtual and physical agents

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SIMULATION MODELLING PRACTICE AND THEORY 2020年 102卷 102058-102058页

作者： Ponce, Hiram Moya-Albor, Ernesto Martinez-Villasemor, Lourdes Brieva, Jorge Univ Panamer Fac Ingn Augusto Rodin 498 Ciudad De Mexico 03920 Mexico

This paper presents a distributed evolutionary learning control based on social wound treatment for mobile robot navigation using an integrated multi-robot system comprised of simulated and physical robots. To do so, this work proposes an extension of the population-based metaheuristic wound treatment optimization (WTO) method into a distributed scheme. In addition, this distributed WTO method is implemented on the multi-robot system allowing them to experience the environment in their own and communicate their findings, resulting in an emergence intelligence. We implemented our proposal using the combination of five simulated robots with one physical robot for tuning a navigation controller to move freely in a workspace. Results showed that the solution found by this multi-robot system aims using the output controller in the physical robot for successfully achieving the goal to move the robot around a U-maze, without applying any transfer learning approach. We consider this proposal useful in evolutionary robotics, and of great importance to decrease the gap related to transfer knowledge in robotics from simulation to reality.

关键词： Multi-robot system evolutionary robotics Metaheuristic optimization Decentralized systems Control navigation

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Volume signalling in real and robot nervous systems

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THEORY IN BIOSCIENCES 2001年第3-4期120卷 253-269页

作者： Husbands, P Philippides, A Smith, T O'Shea, M Univ Sussex COGS Brighton BN1 9QH E Sussex England

This paper presents recent work in computational modelling of diffusing gaseous neuromodulators in biological nervous systems. A variety of interesting and significant properties of such four dimensional neural signalling systems are demonstrated. It is shown that the morphology of the neuromodulator source plays a highly significant role in the diffusion patterns observed. The paper goes on to describe work in adaptive autonomous systems directly inspired by this: an exploration of the use of virtual diffusing modulators in robot nervous systems built from non-standard artificial neural networks. These virtual chemicals act over space and time modulating a variety of node and connection properties in the networks. A wide variety of rich dynamics are possible in such systems;in the work described here, evolutionary robotics techniques have been used to harness the dynamics to produce autonomous behaviour in mobile robots. Detailed comparative analyses of evolutionary searches, and search spaces, for robot controllers with and without the virtual gases are introduced. The virtual diffusing modulators are found to provide significant advantages.

关键词： diffusing neuromodulators nitric oxide evolutionary robotics GasNets artificial gaseous neuromodulators

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Valentino Braitenberg's Table: Downhill Innovation of Vehicles via Darwinian Evolution 13th

Valentino Braitenberg's Table: Downhill Innovation of Vehicl...

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13th International Conference on Computational Collective Intelligence (ICCCI)

作者： Shaghaghi, Sahand Hamid, Owais Nehaniv, Chrystopher L. Univ Waterloo Syst Design Engn Waterloo ON Canada

ISBN: (纸本)9783030881139;9783030881122

Evolution has been a topic of interest that has been explored extensively throughout recent history. Ever since the proposal of the evolutionary theory by Darwin, there have been attempts made to validate, extend, explore and exploit the different aspects of this theory. This study sets forth methods to explore elements of what has been proposed by Darwinian evolution using Braitenberg vehicles, simple machines with sensory-motor couplings in an environmental context. In this study, a simulation environment is set up based upon the descriptions of neurophysiologist Valentino Braitenberg. This simulation environment is then utilized to carry out the thought experiments envisioned by Braitenberg for the simplest nontrivial kinds of vehicles. The methodology and results for the carried out experiments are detailed in this paper. Apart from an understanding of whether the environmental setup affects evolution, specifically the number of light sources (stimuli) and location of these light sources, the experiments show an interesting trend regarding dynamic equilibrium of the evolutionary process, the ramifications of which might not have been understood well enough previously. It is concluded that ecological setup, as well as the initial genetic makeup of the vehicles, play a crucial role in the evolution of vehicles in scenarios laid out in this study. Further, the placement of stimuli (location of lights) and the number of the stimuli have a visible effect on the survivability of vehicle types (species).

关键词： Braitenberg vehicles Multi-agent robotics evolutionary robotics

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Evolution of Station Keeping as a Response to Flows in an Aquatic Robot 13

Evolution of Station Keeping as a Response to Flows in an Aq...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Moore, Jared M. Clark, Anthony J. McKinley, Philip K. Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781450319638

Developing complex behaviors for aquatic robots is a difficult engineering challenge due to the uncertainty of an underwater environment. Neuroevolution provides one method of dealing with this type of problem. Artificial neural networks discern different conditions by mapping sensory input to responses, and evolutionary computation provides a training algorithm suitable to the high dimensionality of the problem. In this paper, we present results of applying neuroevolution to an aquatic robot tasked with station keeping, that is, maintaining a given position despite surrounding water flow. The virtual device exposed to evolution is modeled after a physical counterpart that has been fabricated with a 3D printer and tested in physical environments. Evolved behaviors exhibit a variety of unexpected, complex fin/flipper movements that enable the robot to achieve and maintain station, despite water flow from different directions. Moreover, the results show that evolved controllers are able to effectively carry out this task using only information from a simulated accelerometer and gyroscope, matching the inertial measurement unit (IMU) on the actual robot.

关键词： evolutionary robotics Neural Network Application Simulation Station Keeping Aquatic robotics Neuroevolution

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