Unconstrained evolution and hard consequences

Thompson, Adrian; Harvey, Inman; Husbands, Phil

doi:10.1007/3-540-61093-6_7

Cited by 65 publications

(46 citation statements)

References 12 publications

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“…Examples of morphological specialization include the evolution of optimal arrangements of sensors and actuators in the design of simulated automobiles , (Zhang et al, 2003), evolution of agent morphologies and controllers for various forms of motion in simulated environments (Sims, 2004), evolution of physical electric circuits for control (Thompson, Harvey, & Husbands, 1996), and evolving robot morphology for accomplishing different forms of physical motion (Lipson & Pollack, 2000).…”

Section: Morphological Versus Behavioral Specializationmentioning

confidence: 99%

Emergent Specialization in Biologically Inspired Collective Behavior Systems

Nitschke

Schut

Eiben

2008

Intelligent Complex Adaptive Systems

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Specialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles, where specialization that emerges as a result of system dynamics and is used problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research and future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies. 2 Emergent Specialization in Biologically Inspired Collective Behavior Systems AbstractSpecialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles, where specialization that emerges as a result of system dynamics and is used problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research and future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies.

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Section: Morphological Versus Behavioral Specializationmentioning

confidence: 99%

Emergent Specialization in Biologically Inspired Collective Behavior Systems

Nitschke

Schut

Eiben

2008

Intelligent Complex Adaptive Systems

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“…The term Evolutionary Robotics has been coined by a group of researchers at the University of Sussex (Cli , Harvey, & Husbands, 1993) whose approach is based on a combination of simulations and physical robots guided by e v olutionary Dynamical-Recurrent-Neural-Networks (Harvey, Husbands, Cli , Thompson, & Jakobi, 1997). The Sussex group has developed a new evolutionary paradigm called Species Adaptation Genetic Algorithm (Harvey, 1992(Harvey, , 1993 for incrementally evolving neurocontrollers and patterns of logical gates and connections for recon gurable circuits (Thompson, Harvey, & Husbands, 1996). A r e s e a r c h group at the Italian Research Council in Rome has introduced the concept of Ecological Neural Networks (Parisi et al, 1990) within the framework of evolutionary sensorimotor organisms and carried out a set of experiments that combine new simulation tools (Nol , Floreano, Miglino, & Mondada, 1994b) and physical robots.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary neurocontrollers for autonomous mobile robots

1998

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In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent w ork in the attempt to provide a uni ed picture within which the reader can compare the e ects of systematic variations on the experimental settings. After describing some key principles for building mobile robots and tools suitable for experiments in adaptive robotics, we g i v e a n o verview of di erent approaches to evolutionary robotics and present our methodology. We start reviewing two basic experiments showing that di erent e n vironments can shape very di erent behaviors and neural mechanisms under very similar selection criteria. We then address the issue of incremental evolution in two di erent experiments from the perspective of changing environments and robot morphologies. Finally, w e i n vestigate the possibility o f e v olving plastic neurocontrollers and analyze an evolved neurocontroller that relies on fast and continuously changes synapses characterized by dynamic stability. We conclude by reviewing the implications of this methodology for engineering, biology, cognitive science, and arti cial life, and point at future directions of research.

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“…As a first step one must choose the basic logic gates (e.g., AND, OR, and NOT) and suitably codify them, along with the interconnections between gates, to produce the genome encoding. An example of this approach is offered in [22]. Higuchi et al [23] used a low-level bit string representation of the system's logic diagram to describe small-scale PAL's, where the circuit is restricted to a logic sum of products.…”

Section: ) Genome Encodingmentioning

confidence: 99%

A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

Sipper

Sánchez

Mange

et al. 1997

IEEE Trans. Evol. Computat.

175

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Abstract-If one considers life on Earth since its very beginning, three levels of organization can be distinguished: the phylogenetic level concerns the temporal evolution of the genetic programs within individuals and species, the ontogenetic level concerns the developmental process of a single multicellular organism, and the epigenetic level concerns the learning processes during an individual organism's lifetime. In analogy to nature, the space of bio-inspired hardware systems can be partitioned along these three axes, phylogeny, ontogeny, and epigenesis, giving rise to the POE model. This paper is an exposition and examination of bio-inspired systems within the POE framework, with our goals being: 1) to present an overview of current-day research, 2) to demonstrate that the POE model can be used to classify bioinspired systems, and 3) to identify possible directions for future research, derived from a POE outlook. We first discuss each of the three axes separately, considering the systems created to date and plotting directions for continued progress along the axis in question. We end our exposition by a discussion of possible research directions, involving the construction of bio-inspired systems that are situated along two, and ultimately all three axes. This could give rise to novel systems endowed with evolutionary, reproductive, regenerative, and learning capabilities.

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Unconstrained evolution and hard consequences

Cited by 65 publications

References 12 publications

Emergent Specialization in Biologically Inspired Collective Behavior Systems

Emergent Specialization in Biologically Inspired Collective Behavior Systems

Evolutionary neurocontrollers for autonomous mobile robots

A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

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