Use of context blocks in genetic programming for evolution of robot morphology

Gregor, Michal; Spalek, Juraj; Capak, Jan

doi:10.1109/elektro.2012.6225655

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…Genotype representations in the literature use binary [36][37][38], integer [39], real number [40][41][42] and string [43] encoding with many applying either or a combination of the different encoding methods. The basic genome types could also be placed in a matrix [44], vector [34,[45][46][47], graph [48][49][50][51][52][53] and tree [54][55][56][57][58][59][60][61][62]. Further, in other representations, [34,63] applied the concept of a hox gene for encoding body morphology and [39,64] applied Compositional Pattern-Producing Networks (CPPN) based encoding .…”

Section: Genotypementioning

confidence: 99%

“…Chromosomes are represented as parse trees, and either recombination or mutation is applied instead of both [33]. GP can be found during morphology design in [55,57,79].…”

Section: Different Eas Appliedmentioning

confidence: 99%

“…Similarly, the parameters of CPGs were evolved again by Larpin et al [40]. A GP was used in [63] and [57] to evolve a perceptron (single layer neural network) based controller. Another variation of ANN called Continuous Time Recurrent Neural Networks (CTRNNs) could be seen in [44,59,78] and Elman's recurrent network in [83].…”

Section: Algorithms In Controllermentioning

confidence: 99%

“…Endo et al [90] Humanoid body design GA ANN 2003 Shim et al [69] Wing structure design EA ANN 2004 O'Kelly et al [53] Creatures for combat GA ANN 2004 Macinnes et al [59] Locomotion with LEGO bricks EA CTRNN 2004 Miconi et al [88] Creatures with multiple locomotion modes GA ANN 2005 Lassabe et al [97] Creatures [44] Articulated robot design MOEA CTRNN 2010 Rubrecht et al [81] Serial manipulator design NSGA-II CCC 2011 Azarbadegan et al [51] Biped walking creatures GA ANN 2011 Gregor et al [57] Creatures for locomotion GP ANN 2012 Larpin et al [40] Quadrupedal robot design MOEA CPG 2012…”

Section: Mobile Robotsmentioning

confidence: 99%

See 3 more Smart Citations

A survey on evolutionary-aided design in robotics

Prabhu¹,

Seals²,

Kyberd³

et al. 2018

Robotica

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SUMMARYThe evolutionary-aided design process is a method to find solutions to design and optimisation problems. Evolutionary algorithms (EAs) are applied to search for optimal solutions from a solution space that evolves over several generations. EAs have found applications in many areas of robotics. This paper covers the efforts to determine body morphology of robots through evolution and body morphology with the controller of robots or similar creatures through co-evolution. The works are reviewed from the perspective of how different algorithms are applied and includes a brief explanation of how they are implemented.

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Section: Genotypementioning

confidence: 99%

“…Chromosomes are represented as parse trees, and either recombination or mutation is applied instead of both [33]. GP can be found during morphology design in [55,57,79].…”

Section: Different Eas Appliedmentioning

confidence: 99%

Section: Algorithms In Controllermentioning

confidence: 99%

Section: Mobile Robotsmentioning

confidence: 99%

See 2 more Smart Citations

A survey on evolutionary-aided design in robotics

Prabhu¹,

Seals²,

Kyberd³

et al. 2018

Robotica

View full text Add to dashboard Cite

show abstract

“…Lipson implemented an evolutionary computation method to design and develop different stick-like robots by using bars and actuators as building blocks of structure and artificial neurons as building blocks of control [9]. Gregor used of contexts and context blocks in GP to co-evolve the control and morphology of robots [10]. In hybridizing different evolutionary approaches for optimization to co-evolve controllers and robot bodies, Howard had shown that the idea of hybridizing genetic algorithm with genetic programming is capable of discovering the relationship between the sets of data and perform more efficiently compared to classical evolutionary methods [11].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Evolutionary-Based Optimization of Controller and Morphology of Snake-Like Modular Robots

Chee

Teo

2014

2014 4th International Conference on Artificial Intelligence With Applications in Engineering and Technology

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This paper explores the use of evolutionary algorithm approach to automatically design and optimize the snake-like modular robot to automatically design and optimize the snake-like modular robot to acquire the forward moving behaviour. A hybridized Genetic Programming and selfadaptive Differential Evolution algorithm is implemented to co-evolving both the morphology and controller of the robot throughout the artificial evolutionary process. Two different artificial evolutionary experiments have been conducted in this paper by using the classic DE mutation technique (DE/rand/1/bin) and a customized DE mutation technique with different mutation differential operation. It was found out that the customized DE mutation approach is more effective in coevolving both the morphology and controller for the snake-like modular robot to acquire forward moving behaviour. Moreover, from the analysis conducted on the results obtained throughout the evolutionary process, interesting findings were discovered on the evolved morphology and moving behaviour of the snake-like modular robot. In conclusion, promising results were shown in this work which suggests that the coevolving evolutionary algorithm presented in this work is an alternative method and feasible to be implemented to automatically design and optimize the modular robot for the moving behaviour by co-evolving both the morphology and controller of the modular robot.

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Using LLVM-based JIT compilation in genetic programming

Gregor

Spalek

2016

2016 Elektro

Self Cite

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Abstract-The paper describes an approach to implementing genetic programming, which uses the LLVM library to justin-time compile/interpret the evolved abstract syntax trees. The solution is described in some detail, including a parser (based on FlexC++ and BisonC++) that can construct the trees from a simple toy language with C-like syntax. The approach is compared with a previous implementation (based on direct execution of trees using polymorphic functors) in terms of execution speed.

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Use of context blocks in genetic programming for evolution of robot morphology

Cited by 7 publications

References 5 publications

A survey on evolutionary-aided design in robotics

A survey on evolutionary-aided design in robotics

Simultaneous Evolutionary-Based Optimization of Controller and Morphology of Snake-Like Modular Robots

Using LLVM-based JIT compilation in genetic programming

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