Genetic Relation Algorithm Research Articles

The Genetic Algorithm (GA) in Relation to Natural Evolution


 
 
 
 For optimizing search global solution for complicated issues the Genetic Algorithm (GA) is a famous evolutionary computation technique that plays an important role in finding meaningful solutions to hard problems with a huge search space could be a process based on genetic selection ideas. In addition, it supports machine learning causes, as well as study and evolution. However, developing genetic processes that were formerly significant to a random population, which might be started by biology for chromosomal production with factors like selection, crossover, and mutation. The aim of going through this GA process is to find a solution for consecutive generations. In individual production there has been an extent success instantly in ratio to fitness which is suited for it, as a result successive generation will be better in one condition, which is ensuring the quality. Furthermore, John Holland is considered as being the funding father of the initial genetic algorithm, with a funding date in the 1970s. in this paper we have explained what a genetic algorithm is, its key operations, and how it works as well as its features and applications.
 
 
 
 

Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta

In this paper, Robust Genetic Network Programming (R-GNP) for generating trading rules for stocks is described. R-GNP is a new evolutionary algorithm, where solutions are represented using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid model, R-GNP is applied to generating stock trading rules with variance of fitness values. The unique point is that the generalization ability of R-GNP is improved by using the robust fitness function, which consists of the fitness functions with the original data and a good number of correlated data. Generally speaking, the hybrid intelligent system consists of three steps: priority selection by the portfolio β, optimization by the Genetic Relation Algorithm (GRA), and stock trading by R-GNP. In the simulations, the trading model is trained using the stock prices of 10 brands on the Tokyo Stock Exchange, and then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed R-GNP model obtain much higher profits than the traditional methods even in the world-wide financial crisis of 2007. Hence, its effectiveness has been confirmed.

Optimization of Trusses With Self-Adaptive Approach In Genetic Algorithms

Abstract This paper presents a genetic algorithm method for the optimization of the weight of steel truss structures. In the method of genetic algorithm integer encoding of a discrete set of design variables and novel self-adaptive method based on fuzzy logic mechanism are applied for improving the quality and speed of optimization. Self-adaptive method is applied simultaneously in the selection of chromosomes and to control basic parameters of genetic algorithm. The algorithm proposed in the work was tested on the examples of optimization of steel trusses. Obtained results proved the effectiveness of genetic algorithm in relation to classical genetic algorithm.

Efficiency of Tool Path Optimization Using Genetic Algorithm in Relation to the Optimization Achieved with the CAM Software

The production efficiency has an important role for each manufacturing process, especially in the process of drilling a large number of holes, where production depends on the time required for drilling. The tool path optimization during the drilling is necessary because it leads to increase productivity and to save production costs, especially if the tool that performs drilling operation must visit a significant number of places. The proposed solution is obtained by using a genetic algorithm whereby the optimization problem is reduced to the Traveling Salesman Problem. Problem solution was achieved by using MATLAB software, and the obtained results were compared with the results achieved with CAM software (WinCAM, CAMConcept and CATIA V5).

An improved immigration memetic algorithm for solving the heterogeneous fixed fleet vehicle routing problem

This paper deals with the heterogeneous fixed fleet vehicle routing problem (HFFVRP) which is a generalization of the classical vehicle routing problem (VRP) in the sense that the fixed fleet of vehicles is assumed to be heterogeneous. The objective of HFFVRP is to find the best fleet composition and the collection of routes such that the total costs are minimized. To address this combinatorial optimization problem, we design and implement a hybrid heuristic model integrating a genetic algorithm, a local search mechanism and an immigration strategy. Several strategies for generating the initial population of the genetic algorithm in relation with six local search heuristics are considered. An important feature of the proposed approach refers to the immigration strategy used to ensure diversification by which the level of evolution for the new immigrant individuals increases along with the evolution of the population. The proposed algorithm is tested on a set of HFFVRP benchmark instances and the preliminary results point out that our approach is an attractive and appropriate method to explore the solution space of this complex problem leading to good solutions within reasonable computational times.

Genetic algorithm-based optimized association rule mining for multi-relational data

The multi relational data mining is one of the latest topics in data mining to find the relational patterns. In this paper, we have presented an algorithm for multi-relational rule mining using association rule mining and the optimization process. As a result of the association rule mining on the multirelational data, a number of relevant and irrelevant rules are generated. A rule is specified as a relation between two data points in the dataset. So, an optimization should be done on the mining algorithm in order to get the most relevant rules. We have adapted the technique of genetic algorithm in order to optimize the mined multi relational association rules. The genetic algorithm is one of the best optimization algorithm available and it suites the current problem because of its particular features such as the genetic operators crossover and mutation. The optimization of the rule is done by altering the fitness function of the genetic algorithm in relation with the multi relational data mining algorithm. The results from the experimental analysis showed that the proposed approach has better efficiency over the previous approaches. The most rules optimized is 198 under iterations 10 with a support of 60.

Diversifying risk in portfolios using a variable‐size genetic relation algorithm

AbstractOne important concept in financial risk management is the diversification process of capital allocation. This paper proposes an evolutionary approach for the optimal diversification when making asset allocation using variable‐size genetic relation algorithm (vs‐GRA), whose main role is to model and evolve structures toward effective and diversified portfolios through its graph structure. Simulations using heterogeneous and globally located asset classes in the United States, Europe, and Asia show that the proposed scheme offers competitive economic advantages. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Erratum: A portfolio selection model using genetic relation algorithm and genetic network programming

Erratum: A portfolio selection model using genetic relation algorithm and genetic network programming

A portfolio selection model using genetic relation algorithm and genetic network programming

AbstractIn this paper, new evolutionary computation methods named genetic relation algorithm (GRA) and genetic network programming (GNP) have been applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up the most efficient portfolio, the proposed model considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. Then, the selected portfolio is further optimized by the stock trading model of GNP. In a sense, the proposed model is an integrated intelligent model. A comprehensive analysis of the results is provided, and it is clarified that the proposed model can obtain much higher profits than other traditional methods. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Evolutionary selection of interesting class association rules using genetic relation algorithm

AbstractDuring the last years, several association rule‐based classification methods have been proposed, these algorithms may quickly generate accurate rules. However, the generated rules are often very large in terms of the number of rules and usually complex and hardly understandable for users. Among all the rules generated by the algorithms, only some of them are likely to be of any interest to the domain expert analyzing the data. Most of the rules are either redundant, irrelevant or obvious. In this paper, a new method for selecting the interesting class association rules is proposed by an evolutionary method named genetic relation algorithm. The algorithm evaluates the relevance and interestingness of the discovered association rules by the relationships between the rules in each generation using a specific measure of distance among them giving a reduced set of rules as the result in the final generation. This small rule set has the following properties: (i) accurate as it has at least the same classification accuracy as the complete association rule set, (ii) interesting because of the diversity of rules and (iii) comprehensible because it is more understandable for the users as the number of attributes involved in the rules is also small. The efficiency of the proposed method is compared with other conventional methods including genetic network programming‐based mining using ten databases and the experimental results show that it outperforms others keeping a good balance between the classification accuracy and the comprehensibility of the rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Genetic relation algorithm with guided mutation for the large-scale portfolio optimization

The survey of the relevant literatures shows that there have been many studies for portfolio optimization problems and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite large. But, almost none of these studies deals with genetic relation algorithm (GRA), where GRA is one of the evolutionary methods with graph structure. This study presents an approach to large-scale portfolio optimization problems using GRA with a new operator, called guided mutation. In order to pick up the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GRA. Guided mutation generates offspring according to the average value of correlation coefficients in each individual, which means to enhance the exploitation ability of evolution of GRA. A genetic relation algorithm with guided mutation (GRA/G) for the portfolio optimization is proposed in this paper. Genetic network programming (GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with GRA/G. The results show that GRA/G approach is successful in portfolio optimization.

Efficient Pruning of Class Association Rules Using Statistics and Genetic Relation Algorithm

Association rule mining is one of the most exploited areas in data mining, which includes applications from business to data classification and summarization. Thus, several association rule-based c...

Evolving Asset Portfolios by Genetic Relation Algorithm

Global financial development have opened innumerable risks and opportunities for investments. A global view of the portfolio allocation through diversification brings advantages for the risk allocation in investments. In this paper, an asset allocation framework under the return, risk and liquidity considerations is proposed for short term investment using Genetic Relation Algorithm. Simulations using the stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show that the proposed framework is effective and robust. The efficacy of the proposed method is compared against the relevant constructs in finance and computational fields.

A partitioned portfolio insurance strategy by a relational genetic algorithm

This paper proposes a new portfolio insurance (PI) strategy named partitioned portfolio insurance (PPI) strategy and a new relation-based genetic algorithm named relational genetic algorithm (RGA) to optimize the proposed PPI strategy. Our PPI strategy extends the traditional PI strategy to become a more aggressive one. In our PPI strategy, we attempt to correctly partition the portfolio into several similar sub-portfolios and then insure the sub-portfolios individually. It not only avoids the downside risk, but also further explores the upside profit successfully. In addition, our RGA which adopts a relational encoding and has a set of problem-independent operators is designed to solve the induced portfolio partitioning problem. The relational encoding eliminates the redundancy of previous GA representations for partitioning problems and improves the performance of genetic search. The problem-independent operators we redesigned manipulate the genes without requiring specific heuristics in the process of evolution. Moreover, our RGA works without requiring a preset number of subsets in advance. Experiments for developing optimized PPI strategies by RGA are performed. Experimental results show that our optimized PPI strategies are significantly better than the traditional PI strategy and our RGA works well for solving the portfolio partitioning problem.