Generative Representations for Automated Design of Robots

A method of automated design of complex, modular robots involves an evolutionary process in which generative representations of designs are used. The term generative representations as used here signifies, loosely, representations that consist of or include algorithms, computer programs, and the like, wherein encoded designs can reuse elements of their encoding and thereby evolve toward greater complexity. Automated design of robots through synthetic evolutionary processes has already been demonstrated, but it is not clear whether genetically inspired search algorithms can yield designs that are sufficiently complex for practical engineering. The ultimate success of such algorithms as tools for automation of design depends on the scaling properties of representations of designs. A nongenerative representation (one in which each element of the encoded design is used at most once in translating to the design) scales linearly with the number of elements. Search algorithms that use nongenerative representations quickly become intractable (search times vary approximately exponentially with numbers of design elements), and thus are not amenable to scaling to complex designs. Generative representations are compact representations and were devised as means to circumvent the above-mentioned fundamental restriction on scalability. In the present method, a robot is defined by a compact programmatic form (its generative representation) and the evolutionary variation takes place on this form. The evolutionary process is an iterative one, wherein each cycle consists of the following steps: 1. Generative representations are generated in an evolutionary subprocess. 2. Each generative representation is a program that, when compiled, produces an assembly procedure. 3. In a computational simulation, a constructor executes an assembly procedure to generate a robot. 4. A physical-simulation program tests the performance of a simulated constructed robot, evaluating the performance according to a fitness criterion to yield a figure of merit that is fed back into the evolutionary subprocess of the next iteration. In comparison with prior approaches to automated evolutionary design of robots, the use of generative representations offers two advantages: First, a generative representation enables the reuse of components in regular and hierarchical ways and thereby serves a systematic means of creating more complex modules out of simpler ones. Second, the evolved generative representation may capture intrinsic properties of the design problem, so that variations in the representations move through the design space more effectively than do equivalent variations in a nongenerative representation. This method has been demonstrated by using it to design some robots that move, variously, by walking, rolling, or sliding. Some of the robots were built (see figure). Although these robots are very simple, in comparison with robots designed by humans, their structures are more regular, modular, hierarchical, and complex than are those of evolved designs of comparable functionality synthesized by use of nongenerative representations

Theme preservation and the evolution of representation

Abstract. The identification of mechanisms by which constraints on phenotypic variability are tuned in nature, and the implementation of these mechanisms in Evolutionary Algorithms (EAs) carries the promise of making EAs less “wasteful”. The constraints on phenotypic variability are determined by the way genotypic variability maps to phenotypic variability. This in turn is determined by the way that phenotypes are represented genotypically. We use a formal model of an EA to show that when some part of the genome is mutated with a much lower probability than some other part, representations used to search the phenotype space- and hence the constraints on phenotypic variability- can themselves be thought to evolve. Specifically, we formally analyze a class of mutationonly fitness proportional evolutionary algorithms and show that these evolutionary algorithms implicitly implement what we call subrepresentation evolving multithreaded evolution. These EAs conduct second-order search over a predetermined set of representations and exploit promising representations within this set for first order evolutionary search. We compare our analytical method and results with those employed in schema analysis and note that by examining systems that are simpler than the ones examined in a typical schema analysis (mutation is the only variational operator in our systems), and by changing how we define the subsets of the genotype space that are analyzed, we have obtained results that are more intuitively understandable and are not specific to a particular data-structure. 1

Comparative study on the application of evolutionary optimization techniques to orbit transfer maneuvers

Orbit transfer maneuvers are here considered as benchmark cases for comparing performance of different optimization techniques in the framework of direct methods. Two different classes of evolutionary algorithms, a conventional genetic algorithm and an estimation of distribution method, are compared in terms of performance indices statistically evaluated over a prescribed number of runs. At the same time, two different types of problem representations are considered, a first one based on orbit propagation and a second one based on the solution of Lambert’s problem for direct transfers. In this way it is possible to highlight how problem representation affects the capabilities of the considered numerical approaches

States based evolutionary algorithm

Choosing the suitable representation, the operators and the values of the parameters of an evolutionary algorithm is one of the main problems to design an efficient algorithm for one particular optimization problem. This additional information to the evolutionary algorithm generally is called the algorithm parameter, or parameter. This work introduces a new evolutionary algorithm, States based Evolutionary Algorithm which is able to combine different evolutionary algorithms with different parameters included different representations in order to control the parameters and to take the advantage of each possible evolution algorithm during the optimization process. This paper gives first experimental arguments of the efficiency of the States based EA