This work is a study of neutrality in the context of Evolutionary Computation systems. In particular, we introduce the use of explicit neutrality with an integer string coding scheme to allow neutrality to be measured during evolution. We tested this method on a Boolean benchmark problem.

Abstract. A method for evolving programs that construct multicellular structures (organisms) is described. The paper concentrates on the difficult problem of evolving a cell program that constructs a fixed size French flag. We obtain and analyze an organism that shows a remarkable ability to repair itself when subjected to severe damage. Its behaviour resembles the regenerative power of some living organisms. 1

. The paper studies the role of neutrality in the fitness landscapes associated with the evolutionary design of digital circuits and particularly the three-bit binary multiplier. For the purpose of the study, digital circuits are evolved extrinsically on an array of logic cells. To evolve on an array of cells, a genotype-phenotype mapping has been devised by which neutrality can be embedded in the resulting fitness landscape. It is argued that landscape neutrality is beneficial for digital circuit evolution. 1 Introduction Digital circuit evolution is a process of evolving configurations of logic gates for some prespecified computational program. Often the aim is for a highly efficient electronic circuit to emerge in a population of instances of the program. Digital electronic circuits have been evolved intrinsically [1] and extrinsically [2--6]. The former is associated with an evolutionary process in which each evolved electronic circuit is built and tested on hardware, whil...

A method for evolving a developmental program inside a cell to create multicellular organisms of arbitrary size and characteristics is described.

A review is given of approaches to growing neural networks and electronic circuits. A new method for growing graphs and circuits using a developmental process is discussed. The method is inspired by the view that the cell is the basic unit of biology. Programs that construct circuits are evolved to build a sequence of digital circuits at user specified iterations. The programs can be run for an arbitrary number of iterations so circuits of huge size could be created that could not be evolved. It is shown that the circuit building programs are capable of correctly predicting the next circuit in a sequence of larger even parity functions. The new method however finds building specific circuits more difficult than a non-developmental method.

Abstract. As is typical in evolutionary algorithms, fitness evaluation in GP takes the majority of the computational effort. In this paper we demonstrate the use of the Graphics Processing Unit (GPU) to accelerate the evaluation of individuals. We show that for both binary and floating point based data types, it is possible to get speed increases of several hundred times over a typical CPU implementation. This allows for evaluation of many thousands of fitness cases, and hence should enable more ambitious solutions to be evolved using GP.

We propose building neutral networks in needle-in-haystack fitness landscapes to assist an evolutionary algorithm to perform search. The experimental results on four different problems show that this approach improves the search success rates in most cases. In situations where neutral networks do not give performance improvement, no impairment occurs either.

While the fault repair capability of Evolvable Hardware (EH) approaches have been previously demonstrated, further improvements to fault handling capability can be achieved by exploiting population diversity during all phases of the fault handling process. A new paradigm for online EH regeneration using Genetic Algorithms (GAs) called Consensus Based Evaluation (CBE) is developed where the performance of individuals is assessed based on broad consensus of the population instead of a conventional fitness function. Adoption of CBE enables information contained in the population to not only enrich the evolutionary process, but also support fault detection and isolation. On-line regeneration of functionality is achieved without additional test vectors by using the results of competitions between individuals in the population. Relative fitness measures support adaptation of the fitness evaluation procedure to support graceful degredation even in the presence of unpredictable changes in the operational environment, inputs, or the FPGA application. Application of CBE to FPGA-based multipliers demonstrates 100 % isolation of randomly injected stuck-at faults and evolution of a complete regeneration within 135 repair iterations while precluding the propagation of any discrepant output. The throughput of the system is maintained at 85.35 % throughout the repair process. 1

Introduction Prediction of stocks is generally believed to be a very di#cult task. The process behaves more like a random walk process and time varying. The obvious complexity of the problem paves way for the importance of intelligent prediction paradigms. During the last decade, stocks and futures traders have come to rely upon various types of intelligent systems to make trading decisions [1], [2], [7]. This chapter presents a comparison of two genetic programming techniques (MEP and LGP), an ensemble MEP and LGP, artificial neural network and a neuro-fuzzy system for the prediction of two well-known stock indices namely Nasdaq-100 index of Nasdaq [19] and the S&P CNX NIFTY stock index [20]. Nasdaq-100 index reflects Nasdaq's largest companies across major industry groups, including computer hardware and software, telecommunications, retail/wholesale trade and biotechnology [21]. The Nasdaq-100 index is a modified capitalization-weighted index, which is designed to limit dominati

this paper. think of functionality space is as an enzyme reference system where each location characterises a certain type of enzyme. Any enzyme can be referenced within this space and, consequently, other enzymes can use it to specify the types of enzymes they would prefer to receive input from