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Hyper-heuristics is an emergent technology that has proven to be effective at solving real-world problems. The two main categories of hyper-heuristics are selection and generation. Selection hyper-heuristics select existing low-level heuristics while generation hyper-heuristics create new heuristics. At the inception of the field single point searches were essentially employed by selection hyper-heuristics,...
Evolutionary Multi-objective optimization using Genetic Algorithms (GA) are proven more powerful and efficient methods for optimization of complex digital circuit problems. In this paper, Genetic programming (GP) has been used based on GA to automate the design of the Digital Combinational Circuit. It is desired to minimize the total number of gates used and number of generations for evolved circuit...
Stock market prediction is of immense interest to trading companies and buyers due to high profit margins. Therefore, precise prediction of the measure of increase or decrease of stock prices also plays an important role in buying/selling activities. This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP)...
Some identification methods of the systems are developed, but there is still some space for finding new solutions for such classic and needed procedure as the identification is. For proposed solution in the paper the genetic algorithms are used as optimization method for finding the parameters of difference equation of systems of the system of 2nd order with defined transfer function, but it can be...
The decomposition of problems into smaller elements is a widespread approach. In this paper we consider two approaches that are based over the principle to segmentation to problems for the resolution of resultant sub-components. On one hand, we have Automatically Defined Functions (ADFs), which originally emerged as a refinement of genetic programming for reuse code and modulirize programs into smaller...
Genetic Programming is a widely used technique to solve many optimization problems. The original representation of a solution is a tree structure. To improve its search capability there are many proposals for encoding data structure of a solution of Genetic Programming as a linear code. However there are a few work in comparing between these proposals. This work presents a systematic way to compare...
In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. This paper presents...
We use genetic programming to evolve accurate predictors (fuzzy rules) that are deployed to estimate the tension in a power plant generator. The meta-heuristic is compared to the finite element method that was used to compute estimated tension. In contrast to the finite element method, the fuzzy predictor (once found) approximates the tension in the facility quickly and with sufficient precision.
Fuzzy sets and fuzzy logic can be used for efficient data mining, classification, and value prediction. We propose a genetically evolved fuzzy predictor to estimate the output of a Photovoltaic Power Plant. Photovoltaic Power Plants (PVPPs) are classified as power energy sources with unstable supply of electrical energy. It is necessary to back up power energy from PVPPs for stable electric network...
Genetic Programming (GP) is being proposed as a machine learning technique in design space exploration. An evolutionary but heuristic approach by default, GP basically searches the whole input space for suboptimal values, which often translates into long convergence times, more processing and thus inefficient resource utilization. We propose in this paper a Guided Search Space GP (GSS-GP) approach...
Fuzzy Logic Controllers (FLCS) are rule-based system that successfully incorporate the flexibility of human-decision making by means of the use of fuzzy set theory. This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. A three-stage evolution framework that uses Genetic Programming (GP) and Genetic Algorithms (GAS) evolves...
Fault-detection approaches in autonomic systems typically rely on runtime software sensors to compute metrics for CPU utilization, memory usage, network throughput, and so on. One detection approach uses data collected by the runtime sensors to construct a convex-hull geometric object whose interior represents the normal execution of the monitored application. The approach detects faults by classifying...
The formulation of user queries is an important part of the information retrieval process. In the complex environment of the World Wide Web and other large data collections, it is often not easy for the users to express their information needs in an optimal way. In this paper, we investigate evolutionary algorithms (in particular genetic programming) as a tool for the optimization of user queries...
Fuzzy classifiers and fuzzy rules are powerful tools in data mining and knowledge discovery. In this work, intrusion detection is approached as a data mining task and genetic programming is deployed to evolve fuzzy classifiers for detection of intrusion and security problems. We train the fuzzy classifier on a data set modeled as a fuzzy information retrieval collection and investigate its ability...
This paper presents a genetic programming (GP) based approach for designing classifiers with feature selection using a modified crossover operator. The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. To overcome the difficulties with standard crossover operator,...
In this paper we investigate the application of tree-adjunct grammars to grammatical evolution. The standard type of grammar used by grammatical evolution, context-free grammars, produce a subset of the languages that tree-adjunct grammars can produce, making tree-adjunct grammars, expressively, more powerful. In this study we shed some light on the effects of tree-adjunct grammars on grammatical...
This paper describes a decision system based on rules for the management of a stock portfolio using a mechanism of dynamic learning to select the stocks to be incorporated. This system simulates the intelligent behavior of an investor, carrying out the buying and selling of stocks, such that during each day the best stocks will be selected to be incorporated in the portfolio by reinforcement learning...
Reversible logic is an emerging research area and has attracted significant attention in recent years. Developing systematic logic synthesis algorithms for reversible logic is still an area of research. Unlike other areas of application, there are relatively few publications on applications of genetic programming - (evolutionary algorithms in general) - to reversible logic synthesis. In this paper,...
Genetic Algorithms and Genetic programming have been used extensively in Evolutionary robotics (ER) with the goal of automatic programming of robotic controllers and has shown to be a promising approach. In this paper, we demonstrate the use of Gene Expression Programming, GEP, a newly developed evolutionary algorithm akin to GA and GP, to evolve robotic behaviours. We use the already well known obstacle...
The task of designing manually morphological operators for a given application is not always a trivial one. Genetic programming is a branch of evolutionary computing and it is consolidating as a promising method for applications of digital image processing. The main objective of genetic programming is to discover how computers can learn to solve problems without being programmed for that. In the literature...
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