Evolutionary synthesis and control of chaotic systems

This research deals with the synthesis and control of chaos by means of evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of synthesis of new chaotic system and optimization of its control and to show a new approach of solving this problem and constructing new cost functions operating in "blackbox mode" without previous exact mathematical analysis of the system, thus without knowledge of stabilizing of the target state. Three different cost functions are presented and tested. The optimizations were achieved in several ways, each one for another desired periodic orbit. The evolutionary algorithm, Self-Organizing Migrating Algorithm (SOMA) was used in its four versions. For each version, repeated simulations were conducted to outline the effectiveness and robustness of used method and cost function. Presented results lend weight to the argument, that proposed cost functions give satisfactory results

Evolutionary Synthesis of Fractional Capacitor Using Simulated Annealing Method

Synthesis of fractional capacitor using classical analog circuit synthesis method was described in [6]. The work presented in this paper is focused on synthesis of the same problem by means of evolutionary method simulated annealing. Based on given desired characteristic function as input impedance or transfer function, the proposed method is able to synthesize topology and values of the components of the desired analog circuit. Comparison of the results given in [6] and results obtained by the proposed method will be given and discussed

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

Complexity Theory, Adaptation, and Administrative Law

Recently, commentators have applied insights from complexity theory to legal analysis generally and to administrative law in particular. This Article focuses on one of the central problems that complexity. theory addresses, the importance and mechanisms of adaptation within complex systems. In Part I, the Article uses three features of complex adaptive systems-emergence from self-assembly, nonlinearity, and sensitivity to initial conditions-and explores the extent to which they may add value as a matter of positive analysis to the understanding of change within legal systems. In Part H, the Article focuses on three normative claims in public law scholarship that depend explicitly or implicitly on notions of adaptation: that states offer advantages over the federal government because experimentation can make them more adaptive, that federal agencies should themselves become more experimentalist using the tool of adaptive management, and that administrative agencies shou Id adopt collaborative mechanisms in policymaking. Using two analytic tools found in the complexity literature, the genetic algorithm and evolutionary game theory, the Article tests the extent to which these three normative claims are borne out