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The Advantages of Generative Grammatical Encodings for Physical Design
- In Congress on Evolutionary Computation
, 2001
"... One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final desig ..."
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Cited by 107 (15 self)
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One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding. We describe a system for creating generative specifications by combining Lindenmayer systems with evolutionary algorithms and apply it to the problem of generating table designs. Designs evolved by our system reach an order of magnitude more parts than previous generative systems. Comparing it against a non-generative encoding we find that the generative system produces designs with higher fitness and is faster than the non-generative system. Finally, we demonstrate the ability of our system to go from design to manufacture by constructing evolved table designs using rapid prototyping equipment. 1 Introduction Evolutionary algorithms (EAs) have been succe...
Evolutionary computation in structural design
- Journal of Engineering with Computers
, 2001
"... Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technolog ..."
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Cited by 54 (7 self)
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Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems ’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.
Evolutionary Body Building: Adaptive physical designs for robots
- Artificial Life
, 1998
"... Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of co ..."
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Cited by 47 (8 self)
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Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of co-evolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has usually resulted in virtual entities which are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components which stick together. Evolution takes place in a simulator which computes forces and stresses and predicts stability of 3dimensional brick structures. The final printout of ...
Evolution of Generative Design Systems for Modular Physical Robots
- In IEEE International Conference on Robotics and Automation
, 2001
"... Recent research has demonstrated the ability for automatic design of the morphology and control of real physical robots using techniques inspired by biological evolution. The main criticism of the evolutionary design approach, however, is that it is doubtful whether it will reach the high complexiti ..."
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Cited by 34 (12 self)
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Recent research has demonstrated the ability for automatic design of the morphology and control of real physical robots using techniques inspired by biological evolution. The main criticism of the evolutionary design approach, however, is that it is doubtful whether it will reach the high complexities necessary for practical engineering. Here we claim that for automatic design systems to scale in complexity the designs they produce must be made of re-used modules. Our approach is based on the use of a generative design grammar subject to an evolutionary process. Unlike a direct encoding of a design, a generative design specication can re-use components, giving it the ability to create more complex modules from simpler ones. Re-used modules are also valuable for improved eciency in testing and construction. We describe a system for creating generative specications capable of hierarchical modularity by combining Lindenmayer systems with evolutionary algorithms. Using this system we dem...
Three Generations of Automatically Designed Robots
- Artificial Life
, 2001
"... The difficulties associated with designing, building and controlling robots have led their development to a stasis: applications are limited mostly to repetitive tasks with predefined behavior. Over the last few years we have been trying to address this challenge through an alternative approach: ..."
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Cited by 34 (7 self)
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The difficulties associated with designing, building and controlling robots have led their development to a stasis: applications are limited mostly to repetitive tasks with predefined behavior. Over the last few years we have been trying to address this challenge through an alternative approach: Rather than trying to control an existing machine or create a general-purpose robot, we propose that both the morphology and the controller should evolve at the same time. This process can lead to the automatic design of special purpose mechanisms and controllers for specific short-term objectives. Here we provide a brief review of three generations of our recent research, underlying the robots shown on the cover of this issue: Automatically designed static structures, automatically designed and manufactured dynamic electromechanical systems, and modular robots automatically designed through a generative DNA-like encoding. 1 1
Topological Optimum Design using Genetic Algorithms
- Control and Cybernetics
, 1996
"... Structural topology optimization is addressed through Genetic Algorithms: A set of designs is evolved following the Darwinian survival-of-fittest principle. The goal is to optimize the weight of the structure under displacement constraints. This approach demonstrates high flexibility, and breaks man ..."
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Cited by 31 (7 self)
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Structural topology optimization is addressed through Genetic Algorithms: A set of designs is evolved following the Darwinian survival-of-fittest principle. The goal is to optimize the weight of the structure under displacement constraints. This approach demonstrates high flexibility, and breaks many limits of standard optimization algorithms, in spite of the heavy requirements in term of computational effort: Alternate optimal solutions to the same problem can be found; Structures can be optimized with respect to multiple loadings; The prescribed loadings can be applied on the unknown boundary of the solution, rather than on the fixed boundary of the design domain; Different materials as well as different mechanical models can be used, as witnessed by the first results of Topological Optimum Design ever obtained in the large displacements model. But these results could not have been obtained without careful specific handling of the specific aspects of topological genetic optimization: First, specific genetic operators (crossover, mutation) were introduced; Second, special attention was paid to the design of the objective function; The nonlinear geometrical effects of the large displacement model lead to non viable solutions, unless some constraints are imposed on the stress field. 1
Alternative random initialization in genetic algorithms
- Proceedings of the 7 th International Conference on Genetic Algorithms
, 1997
"... Though unanimously recognized as a crucial step in Evolutionary Algorithms, initialization procedures have not been paid much attention so far. In bitstring Genetic Algorithms, for instance, the standard 0/1 equiprobable choice for every bit is rarely discussed, as the resulting distribution probabi ..."
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Cited by 17 (11 self)
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Though unanimously recognized as a crucial step in Evolutionary Algorithms, initialization procedures have not been paid much attention so far. In bitstring Genetic Algorithms, for instance, the standard 0/1 equiprobable choice for every bit is rarely discussed, as the resulting distribution probability over the whole bitstring space is uniform. However, uniformity is relative to a measure on the search space. First, considering the measure given by the density of 1's, the Uniform Covering initialization procedure is naturally designed. Second, taking into account the probability of appearance of sequences of identical bits leads to design another alternative initialization procedure, the Homogeneous Block procedure. These procedures are compared with the standard initialization procedure on several problems. A priori comparison is achieved using FitnessDistance Correlation. Actual experiments demonstrate the accuracy of these FDCbased comparisons, and emphasize the usefulness of the two proposed procedure. 1
Mechanical Inclusions Identification by Evolutionary Computation.
, 1996
"... The problem of the identification of mechanical inclusion is theoretically ill-posed, and to-date numerical algorithms have demonstrated to be inaccurate and unstable. On the other hand, Evolutionary Algorithms provide a general approach to inverse problem solving. However, great care must be taken ..."
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Cited by 12 (5 self)
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The problem of the identification of mechanical inclusion is theoretically ill-posed, and to-date numerical algorithms have demonstrated to be inaccurate and unstable. On the other hand, Evolutionary Algorithms provide a general approach to inverse problem solving. However, great care must be taken during the implementation: The choice of the representation, which determines the search space, is critical. Three representations are presented and discussed. Whereas the straightforward mesh-dependent representation suffers strong limitations, both mesh-independent representation provide outstanding results on simple instances of the identification problem, including experimental robustness in presence of noise.
Investigating coverage and connectivity trade-offs in wireless sensor networks: The benefits of MOEAs
- In Proc. 19th Int’l Conference on Multiple Criteria Decision Making (MCDM 2008
, 2008
"... Abstract How many wireless sensor nodes should be used and where should they be placed in order to form an optimal wireless sensor network (WSN) deployment? This is a difficult question to answer for a decision maker due to the conflicting objectives of deployment costs and wireless transmission rel ..."
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Cited by 6 (2 self)
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Abstract How many wireless sensor nodes should be used and where should they be placed in order to form an optimal wireless sensor network (WSN) deployment? This is a difficult question to answer for a decision maker due to the conflicting objectives of deployment costs and wireless transmission reliability. Here, we address this problem using a multiobjective evolutionary algorithm (MOEA) which allows to identify the trade-offs between low-cost and highly reliable deployments— providing the decision maker with a set of good solutions to choose from. For the MOEA, we use an off-the-shelf selector and propose a problem-specific representation, an initialization scheme, and variation operators. 1
Identification of Mechanical Inclusions
- Evolutionary Computation in Engeneering, 477--494
, 1997
"... Evolutionary Algorithms provide a general approach to inverse problem solving: As optimization methods, they only require the computation of values of the function to optimize. Thus, the only prerequisite to efficiently handle inverse problems is a good numerical model of the direct problem, and a r ..."
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Cited by 4 (3 self)
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Evolutionary Algorithms provide a general approach to inverse problem solving: As optimization methods, they only require the computation of values of the function to optimize. Thus, the only prerequisite to efficiently handle inverse problems is a good numerical model of the direct problem, and a representation for potential solutions. The identification of mechanical inclusion, even in the linear elasticity framework, is a difficult problem, theoretically ill-posed: Evolutionary Algorithms are in that context a good tentative choice for a robust numerical method, as standard deterministic algorithms have proven inaccurate and unstable. However, great attention must be given to the implementation. The representation, which determines the search space, is critical for a successful application of Evolutionary Algorithms to any problem. Two original representations are presented for the inclusion identification problem, together with the associated evolution operators (crossover and muta...
