Search By Subject

We present IGAP, a peer to peer interactive genetic algorithm which reflects the real world methodology followed in team design. We apply our methodology to floorplanning. Through collaboration users are able to visualize designs done by peers on the ...

The intention of the present work is to apply data mining and PSO to propose the solution of a specific problem about society modelling. We analyze the voting behavior and ratings of judges in a popular song contest held every year in Europe. The ...

Genetic Network Programming (GNP) has been proposed as a graph-based evolutionary algorithm. GNP works well especially in dynamic environments due to its graph structures. In addition, a stock trading model using GNP with Importance Index (GNP-IMX) has ...

The use of a-priori information, where available, is an important step in solving an already computationally expensive tomographic imaging problem [1]. Here, an enhanced genetic algorithm based reconstruction technique is proposed that is capable of ...

This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images. The algorithm is based on a recently developed swarm-intelligence technique, well known as the Bacterial Foraging Optimization (BFO). A ...

Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named "Genetic Network Programming" (GNP) has been developed and applied to the stock market recently. In this paper a portfolio ...

BT workforce scheduling problem considers technicians (with different skills) which are assigned to tasks which arrive (partially) dynamically during the day. In order to manage their workforce, BT divides the different regions into several areas. In ...

Stock selection for hedge fund portfolios is a challenging problem that has previously been tackled by many machine-learning, genetic and evolutionary systems, including both Genetic Programming (GP) and Support Vector Machines (SVM). But which is the ...

A major difficulty for anomaly detection lies in discovering boundaries between normal and anomalous behavior, due to the deficiency of abnormal samples in the training phase. In this paper, a novel coevolutionary algorithm which attempts to simulate ...

The bid-offer spread on equity options is a key source of profits for market makers, and a key cost for those trading in the options. Spreads are influenced by dynamic market factors, but is there also a predictable element and can Genetic Programming ...

Genetic Programming (GP) has proved its applicability for time series forecasting in a number of studies. The Dynamic Forecasting Genetic Program (DyFor GP) model builds on the GP technique by adding features that are tailored for the forecasting of ...

In this paper we investigated the use of Genetic Programming (GP) to evolve programs which could detect moving objects in videos. Two main approaches under the paradigm were proposed and investigated, single-frame approach and multi-frame approach. The ...

In this work a branch prediction system which utilizes evolutionary techniques is introduced. It allows the predictor to adapt to the executed code and thus to improve its performance on the fly. Experiments with the predictor system were performed and ...

In this paper a music composition system based on genetic algorithms (GAs) will be presented. It can create multi-instrumental, guitar-orientated rock music using objective measures for its fitness functions. The output of this system is a song in the ...

The Frequency Assignment Problem (FAP) is one of the key issues in the design of GSM networks (Global System for Mobile communications), and will remain important in the foreseeable future. There are many versions of FAP, most of them benchmarking-like ...

In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is becoming much more complex. Subtle risk decisions may often need to be made and people are not always ...

In this paper, we report on work done evolving Bayesian Networks with Genetic Algorithms. We use a Chain Model GA [19] to induce a Bayesian network model for the real world problem of Prostate Cancer management. Bayesian networks can and have been used ...

Scheduling collective communications (CC) in networks based on optimal graphs and digraphs has been done with the use of the evolutionary techniques. Inter-node communication patterns scheduled in the minimum number of time slots have been obtained. ...

Multiobjective methods are ideal for evolving a set of portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk, and an investor can choose her preferred point on the risk-return frontier. However, there are ...

Military missions are turning to more complicated and advanced automation technology for maximum endurance and efficiency as well as the minimum vital risks. The path planners which generate collision-free and optimized paths are needed to give ...