1,054 research outputs found
Evolution of Neural Networks for Physically Simulated Evolved Virtual Quadruped Creatures
This work develops evolved virtual creatures (EVCs) using neuroevolution as the controller for movement and decisions within a 3D physics simulated environ-ment. Previous work on EVCs has displayed various behaviour such as following a light source. This work is focused on complexifying the range of behaviours available to EVCs. This work uses neuroevolution for learning specific actions combined with other controllers for making higher level decisions about which action to take in a given scenario. Results include analysis of performance of the EVCs in simulated physics environment. Various controllers are compared including a hard coded benchmark, a fixed topology feed forward artificial neural network and an evolving ANN subjected to neuroevolution by applying mutations in both topology and weights. The findings showed that both fixed topology ANNs and neuroevolution did successfully control the evolved virtual creatures in the distance travelling task
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A survey on evolutionary-aided design in robotics
The 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
Co-evolution of morphology and control in developing structures
The continuous need to increase the efficiency of technical systems requires the utilization of complex adaptive systems
which operate in environments which are not completely predictable. Reasons are often random nature of the environment
and the fact that not all phenomena which influence the performance of the system can be explained in full detail. As a consequence, the developer often gets
confronted with the task to design an adaptive system with the lack of prior knowledge about the problem at hand.
The design of adaptive systems, which react autonomously to changes in their environment, requires the coordinated
generation of sensors, providing information about the environment, actuators which change the current state of the system
and signal processing structures thereby generating suitable reactions to changed conditions. Within the scope of the thesis, the new system growth method has been introduced. It is based on the evolutionary optimization design technique, which can automatically produce the efficient systems with optimal initially non-defined configuration. The final solutions produced by the novel growth method have low dimensional perception, actuation and signal processing structures optimally adjusted to each other during combined evolutionary optimization process.
The co-evolutionary system design approach has been realized by the concurrent development and gradual complexification of the sensory, actuation and corresponding signal processing systems during entire optimization.
The evolution of flexible system configuration is performed with the standard evolutionary strategies by means of adaptable representation of variable length and therewith variable complexity of the system which it can represent in the further optimization progress. The co-evolution of morphology and control of complex adaptive systems has been successfully performed for the examples of a complex aerodynamic problem of a morphing wing and a virtual intelligent autonomously driving vehicle.
The thesis demonstrates the applicability of the concurrent evolutionary design of the optimal morphological configuration, presented as sensory and actuation systems, and the corresponding optimal system controller. Meanwhile, it underlines the potentials of direct genotype â phenotype encodings for the design of complex engineering real-world applications.
The thesis argues that often better, cheaper, more robust and adaptive systems can be
developed if the entire system is the design target rather than its separate functional
parts, like sensors, actuators or controller structure. The simulation results demonstrate that co-evolutionary methods are able to generate systems which can optimally adapt to the unpredicted environmental conditions while at the same time shedding light on the precise synchronization of all functional system parts during its co-developmental process
Development, evolution and genetic analysis of C. elegans-inspired foraging algorithms under different environmental conditions
In this work 3 minimalist bio-inspired foraging algorithms based on C. elegansâ chemotaxis and foraging behaviour were developed and investigated. The main goal of the work is to apply the algorithms to robots with limited sensing capabilities. The refined versions of these algorithms were developed and optimised in 22 different environments. The results were processed using a novel set of techniques presented here, named Genotype Clustering. The results lead to two distinct conclusions, one practical and one more academic. From a practical perspective, the results suggest that, when suitably tuned, minimalist C. elegans-inspired foraging algorithms can lead to effective navigation to unknown targets even in the presence of repellents and under the influence of a significant sensor noise. From an academic perspective, the work demonstrates that even simple models can serve as an interesting and informative testbed for exploring fundamental evolutionary principles. The simulated robots were grounded in real hardware parameters, aiming at future application of the foraging algorithms in real robots. Another achievement of the project was the development of the simulation framework that provides a simple yet flexible program for the development and optimisation of behavioural algorithms
Some resonances between Eastern thought and Integral Biomathics in the framework of the WLIMES formalism for modelling living systems
Forty-two years ago, Capra published âThe Tao of Physicsâ (Capra, 1975). In this book (page 17) he writes: âThe exploration of the atomic and subatomic world in the twentieth century has âŠ. necessitated a radical revision of many of our basic conceptsâ and that, unlike âclassicalâ physics, the sub-atomic and quantum âmodern physicsâ shows resonances with Eastern thoughts and âleads us to a view of the world which is very similar to the views held by mystics of all ages and traditions.â This article stresses an analogous situation in biology with respect to a new theoretical approach for studying living systems, Integral Biomathics (IB), which also exhibits some resonances with Eastern thought. Stepping on earlier research in cybernetics1 and theoretical biology,2 IB has been developed since 2011 by over 100 scientists from a number of disciplines who have been exploring a substantial set of theoretical frameworks. From that effort, the need for a robust core model utilizing advanced mathematics and computation adequate for understanding the behavior of organisms as dynamic wholes was identified. At this end, the authors of this article have proposed WLIMES (Ehresmann and Simeonov, 2012), a formal theory for modeling living systems integrating both the Memory Evolutive Systems (Ehresmann and Vanbremeersch, 2007) and the Wandering Logic Intelligence (Simeonov, 2002b). Its principles will be recalled here with respect to their
resonances to Eastern thought
The Current State of Cephalopod Science and Perspectives on the Most Critical Challenges Ahead From Three Early-Career Researchers
International audienceHere, three researchers who have recently embarked on careers in cephalopod biology discuss the current state of the field and offer their hopes for the future. Seven major topics are explored genetics, aquaculture, climate change, welfare, behavior, cognition, and neurobiology. Recent developments in each of these fields are reviewed and the potential of emerging technologies to address specific gaps in knowledge about cephalopods are discussed. Throughout, the authors highlight specific challenges that merit particular focus in the near-term. This review and prospectus is also intended to suggest some concrete near-term goals to cephalopod researchers and inspire those working outside the field to consider the revelatory potential of these remarkable creatures
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