Evolutionary computation

Computational inspiration is a process of using natural evolution and growth processes to develop similar computational processes that can be used in computer programs for the automated development of design concepts, including inventive design concepts. 

We will focus on two specific forms of computational inspiration that are particularly promising for inventive engineers. First, we will discuss evolutionary computation (EC), which is inspired by the mechanisms of the evolution of natural systems. Next, we will overview cellular automata, which are inspired by the mechanisms of growth in natural systems. 

From an engineering perspective, natural selection can be explained as a multicriteria selection process involving criteria like energy consumption, weight, size, developmental costs, speed, body protection, maneuverability, and so on. All these criteria contribute in a complex way to the survivability or to the fitness of a given offspring. The other engineering interpretation of natural selection is in the context of utility theory (Keeney and Raiffa 1993). In this case, utility functions are established for all-important 

Natural selection will work effectively only when a sufficient variety of organisms exists within a population. This variety of organisms is called genetic variation, and nature has several reproduction mechanisms that contribute to this variation. They include mutation, genetic recombination, and bringing in genetic material from a different population. All these mechanisms operate on the level of genotypes and allies. 

A design concept will be called an individual when it is viewed as a member of a population, but it will be called an offspring or child when it is considered as a product of the reproduction process. 

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GAs or other methods from evolutionary computation are applied in various fields of chemistry Its tasks include the geometry optimization of conformations of small molecules, the elaboration of models for the prediction of properties or biological activities, the design of molecules de novo, the analysis of the interaction of proteins and their ligands, or the selection of descriptors [18]. The last application is explained briefly in Section 9.7.6. 

Horn, J. (1997) Multicriteria Decision Making. In Back, T., Fogel, D.B., Ichalewicz, Z. (eds) Handbook of Evolutionary Computation. Institute of Physics Publishing, Bristol, UK. 

Cartwright HM (2004) An Introduction to Evolutionary Computation and Evolutionary Algorithms 110 1-32... 

Gillet VJ (2004) Applications of Evolutionary Computation in Drug Design 110 133-152 Glazer EC, see Contakes SM (2007) 123 177-203... 

Harris KDM, Johnston RL, Habershon S (2004) Application of Evolutionary Computation in Structure Determination from Diffraction Data 110 55-94 Hartke B (2004) Application of Evolutionary Algorithms to Global Cluster Geometry Optimization 110 33-53... 

Evolutionary computation which is learned by watching population dynamics the most important programming are genetic algorithms which are inspired by the evolutionary processes of mutation, recombination, and natural selection in biology. 

Lucasius, CB (1994) Evolutionary computation a distinctive form of natural computation with chemometric potential. Chapter 9 in Buydens LM, Meissen WJ Chemometrics. Exploring and exploiting chemical information. Katholieke Universiteit Nijmegen... 

Johnston, R.L., et al., Application of genetic algorithms in nanoscience Cluster geometry optimisation. Applications of Evolutionary Computing, Proceedings, Lecture Notes in Computer Science, Springer, Berlin, 2279, 92, 2002. 

A couple of more recent texts provide gentle introductions to the subject. Evolutionary Computation by De Jong12 looks beyond the basic genetic algorithm, but does so at a studied pace, thus is suitable for those who do not yet have much experience in the field. Finally, Swarm Intelligence, by Kennedy and... 

De Jong, K.A., Evolutionary computation A unified approach, MIT Press, Cambridge, MA, 2006. 

Despite a mass of research activity in evolutionary computation (EC), activity that has led to solid theoretical results and realistic applications, there are still a number of perennial irritations that almost all EC techniques suffer from. First, the serious computational cost of evaluating numerous potential solutions (or individuals), over hundreds of iterations (or generations), places pragmatic and sometimes formal limitations on the use of EC in real-world applications with time-sensitive outputs, such as online multiprocessor scheduling. This real limitation deters many potential users from using or even considering the use of EC in heavy-duty engineering and scientific applications. 

Representation requires that the designer of a typical evolutionary computation algorithm (EA) formulates one inadaptable blueprint for the solution of some problem, then present the variables of that blueprint in a form that is amenable to manipulation by the genetic operators of the EA. Fitness evaluation, on the other hand, has limited GA in two distinct ways (1) it has limited environmental feedback to the confines of a formula or algorithm, which reflects accurately and exclusively the quality of the complete candidate solution from the perspective of the human designer. In addition, (2) fitness evaluation has proven to be the most computationally costly part of a typical EA. Note that elaborate developmental mappings actually increase that computational cost. However, our interest here lies in the limiting effects of representation. 

Kumar, S. and Bentley, P.J. (1999) Computational embryology Past, present and future. In Ghosh, A. and Tsutsui, S. (eds.) Theory and Application of Evolutionary Computation Recent Trends, Springer-Verlag, Berlin. 

Eiben, A.E. and Smith, J.E. (2007) Introduction to Evolutionary Computing, Springer-Verlag, Berlin. 

Genetic programming, a specific form of evolutionary computing, has recently been used for predicting oral bioavailability [23], The results show a slight improvement compared with the ORMUCS Yoshida-Topliss approach. This supervised learning method and other described methods demonstrate that at least qualitative (binned) predictions of oral bioavailability seem tractable directly from the structure. 

J., Evolutionary computational methods to predict oral bioavailability QSPRs, Curr. Opin. Drug Disc. Dev. 2002, 5, 44-51. 

Glover, F., Laguna, M. and Marti, R. (2003) Scatter search, in Advances in Evolutionary Computation Theory and Applications, (eds A. Ghosh and S. Tsut-sui), Springer, New York, pp. 519-537. 

Z. (eds.) (1997) Handbook of Evolutionary Computation, Oxford University Press, New York. 

Stephan, C. and Sullivan, J. (2004). An agent-based hydrogen vehicle/infrastructure model. Evolutionary Computation (CEC) 2004, 2, 1774-1779. 

Eogel, D.B. Evolutionary Computation - Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway,... 

Figure 8.9 Performance of best evolved individual on three different fitness cases. The images in the first row show the reflectance images. The images in the second row show the virtual illuminant. The images in the third row show the input images presented to the individual. The images in the fourth row show the illuminant that was estimated by the evolved individual. In the last row, the estimated reflectance is shown. (Reproduced from Ebner M. 2006 Evolving color constancy. Special issue on evolutionary computer vision and image understanding of pattern recognition letters, Elsevier, 27(11), 1220-1229, by permission from Elsevier.)...

Ebner M 2006 Evolving color constancy. Special Issue on Evolutionary Computer Vision and Image Understanding of Pattern Recognition Letters 27(11), 1220-1229. 

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