Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2008: E Mario Giacobini,Anthony Brabazon,Shengxiang Yang Conference proceedings 2008 S

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Encyclopedia of Machine Learnings a self-learning, non-greedy optimization technique for network topology design. It generates new solutions based on the merit of the preceding ones. This is achieved by maintaining a solution library for all the variables. Based on certain heuristics, the library is updated after each set of gener

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https://doi.org/10.1007/978-0-387-30164-8omated portscan detection. These schemes include a simple Fuzzy Inference System (FIS) that uses classical inductive learning, a Neural Network that uses back propagation algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) that also employs back propagation algorithm. We carry out an unbi

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Encyclopedia of Machine Learningrules are evolved using two different representations. The first is the classical single tree approach, while the second is a dual tree structure where evaluation is contingent on the current market position. Hence, buy and sell rules are co-evolved. Both methods are capable of discovering significa

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Agent-Based Computational Modelsnd the strict solution. In the paper the evolutionary system for generating investment strategies is presented. The algorithms used in the system (evolutionary algorithm, co-evolutionary algorithm, and agent-based co-evolutionary algorithm) are verified and compared on the basis of the results comin

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Ancillary

Encyclopedia of Machine Learning focuses on the case of time–dependent demand and backlogging rates, limited or infinite warehouse capacity and taking into account the time value of money. The algorithm is based on Particle Swarm Optimization and it is capable of computing the number of replenishment cycles as well as the correspo

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Reference work 2017Latest edition of E.coli bacteria. This paper illustrates how a BFO algorithm can be constructed and applied to solve parameter estimation of a EGARCH-M model which is then used for calibration of a volatility option pricing model. The results from the algorithm are shown to be robust and extendable, suggesting t

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Adaptive Real-Time Dynamic Programmingnt of data. The Kohonen’s self organizing map (SOM) are usually used to find unknown relationships between a set of variables that describe a problem, and to identify those with higher significance. In this work we have used genetic programming (GP) to produce models that can predict if a company is

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Adaptive Real-Time Dynamic Programming of distribution algorithms (EDAs). The study assesses the performance of the QIEA on a series of benchmark problems and compares the results with those from a canonical genetic algorithm. Furthermore, we apply QIEA to a finance problem, namely non-linear principal component analysis of implied vola