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A Framework for the Automatic Identification and Extraction of Computation from Materials
"... Abstract. This paper describes our intentions on how to advance the field of exploiting physical systems for computation. We identify two major issues in unconventional computation. The first is finding a system that can be used as a “physical processor”. The requirements are unclear, as we have lim ..."
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Abstract. This paper describes our intentions on how to advance the field of exploiting physical systems for computation. We identify two major issues in unconventional computation. The first is finding a system that can be used as a “physical processor”. The requirements are unclear, as we have limited experience in exploiting the unknown for computation. The second is determining the most appropriate ways to program and interface to “physical processor”, again this stems from our lack of experience with working with systems that we have neither designed or have full knowledge of their operation. We discuss a possible mechanism that will allow us to go from identification of suitable computational materials through to practical exploitation of their computational abilities. 1
A Bacterial Chemotaxis Multiobjective Optimization Algorithm
"... This paper presents a multiobjective optimization algorithm based on bacterial chemotaxis BCMOA (Bacterial Chemotaxis Multiobjective Optimization Algorithm), it incorporates chemotactical responses of individuals as a viable bioinspired optimization technique. As other multiobjective optimization al ..."
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This paper presents a multiobjective optimization algorithm based on bacterial chemotaxis BCMOA (Bacterial Chemotaxis Multiobjective Optimization Algorithm), it incorporates chemotactical responses of individuals as a viable bioinspired optimization technique. As other multiobjective optimization algorithms, the BCMOA works with populations, in this case bacterial colonies, which are located into the search space. The locations represent the value of the objective functions for possible solutions to the optimization problem, thus the well known non-dominated sorting concept is incorporated in order to classify the location of each bacterium. The bacterial chemotactic behavior is exploited by using the ability to sense the environmental conditions and make temporal-space comparisons to the actual and previous locations, always with the goal of finding nutrient sources (optimal locations) and escape from noxious environments (dominated locations). The self organized collective behavior showed for bacterial colonies through communications and signaling between bacteria are also considered. The bacterium motile behavior is emulated by application of simple responses to the signal sense in a way that each bacterium develops one of two possible chemotactical actions, swimming or tumbling. The proposed algorithm is validated using the standard test problems SCH, FON, POL, KUR, ZDT1 to ZDT4, and different performance measures were also implemented to compare the performance of BCMOA with the popular algorithm NSGA-II. 2. Keywords: multiobjective optimization, bacterial chemotaxis, bioinspired methods.