Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-1290-6.ch009
A. V. Krishna, Shriansh Pandey, Raghav Sarda
In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates.
{"title":"A Secured Predictive Analytics Using Genetic Algorithm and Evolution Strategies","authors":"A. V. Krishna, Shriansh Pandey, Raghav Sarda","doi":"10.4018/978-1-7998-1290-6.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-1290-6.ch009","url":null,"abstract":"In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122063401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch053
Victer Paul, Ganeshkumar C., Jayakumar L
Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.
{"title":"Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment","authors":"Victer Paul, Ganeshkumar C., Jayakumar L","doi":"10.4018/978-1-7998-8048-6.ch053","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch053","url":null,"abstract":"Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125625383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch032
Heba F. Eid
Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naïve Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.
{"title":"Application of Computational Intelligence in Network Intrusion Detection","authors":"Heba F. Eid","doi":"10.4018/978-1-7998-8048-6.ch032","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch032","url":null,"abstract":"Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naïve Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"79 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120875682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-3355-0.ch009
Driss Ait Omar, Mohamed EL Amrani, Hamid Garmani, Mohamed Baslam, M. Fakir
Optimization is an essential tool in the field of decision support. In this chapter, the authors study an inverse problem applied in the telecommunication networks. Indeed, in the telecommunication networks, service providers have subscription offers to customers. Since competition is strong in this sector, most of these advertising offerings, totally or partially ambiguous, are prepared to attract the attention of consumers. For this reason, customers face problems in making decisions about the choice of the operators that gives them a better report price/QoS. Mathematical modeling of this decision support problem led to the resolution of an inverse problem. More precisely, the inverse problem is to find the function of the QoS real knowing the QoS theoretical or advertising. This model will help customers who seek to know the degree of sincerity of their operators, and it is an opportunity for operators who want to maintain their resources so that they gain the trust of customers.
{"title":"Decision Choice Optimization With Genetic Algorithm in Communication Networks","authors":"Driss Ait Omar, Mohamed EL Amrani, Hamid Garmani, Mohamed Baslam, M. Fakir","doi":"10.4018/978-1-7998-3355-0.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-3355-0.ch009","url":null,"abstract":"Optimization is an essential tool in the field of decision support. In this chapter, the authors study an inverse problem applied in the telecommunication networks. Indeed, in the telecommunication networks, service providers have subscription offers to customers. Since competition is strong in this sector, most of these advertising offerings, totally or partially ambiguous, are prepared to attract the attention of consumers. For this reason, customers face problems in making decisions about the choice of the operators that gives them a better report price/QoS. Mathematical modeling of this decision support problem led to the resolution of an inverse problem. More precisely, the inverse problem is to find the function of the QoS real knowing the QoS theoretical or advertising. This model will help customers who seek to know the degree of sincerity of their operators, and it is an opportunity for operators who want to maintain their resources so that they gain the trust of customers.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122194145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-3129-6.CH003
Anitha Mary Xavier
Environmental regulations demand efficient and eco-friendly ways of power generation. Coal continues to play a vital role in power generation because of its availability in abundance. Power generation using coal leads to local pollution problems. Hence this conflicting situation demands a new technology - Integrated Gasification Combined Cycle (IGCC). Gasifier is one of the subsystems in IGCC. It is a multivariable system with four inputs and four outputs with higher degree of cross coupling between the input and output variables. ALSTOM – a multinational and Original Equipment Manufacturer (OEM) - developed a detailed nonlinear mathematical model, validated made this model available to the academic community and demanded different control strategies which will satisfy certain stringent performance criteria during specified disturbances. These demands of ALSTOM are well known as “ALSTOM Benchmark Challenges”. The chapter is addressed to solve Alstom Benchmark Challenges using Proportional-Integral-Derivative-Filter (PIDF) controllers optimised by Genetic Algorithm.
{"title":"Genetic-Algorithm-Based Performance Optimization for Non-Linear MIMO System","authors":"Anitha Mary Xavier","doi":"10.4018/978-1-5225-3129-6.CH003","DOIUrl":"https://doi.org/10.4018/978-1-5225-3129-6.CH003","url":null,"abstract":"Environmental regulations demand efficient and eco-friendly ways of power generation. Coal continues to play a vital role in power generation because of its availability in abundance. Power generation using coal leads to local pollution problems. Hence this conflicting situation demands a new technology - Integrated Gasification Combined Cycle (IGCC). Gasifier is one of the subsystems in IGCC. It is a multivariable system with four inputs and four outputs with higher degree of cross coupling between the input and output variables. ALSTOM – a multinational and Original Equipment Manufacturer (OEM) - developed a detailed nonlinear mathematical model, validated made this model available to the academic community and demanded different control strategies which will satisfy certain stringent performance criteria during specified disturbances. These demands of ALSTOM are well known as “ALSTOM Benchmark Challenges”. The chapter is addressed to solve Alstom Benchmark Challenges using Proportional-Integral-Derivative-Filter (PIDF) controllers optimised by Genetic Algorithm.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132796812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch057
Poonam Mishra
Inventory and supply chain management is a real concern for business community in today's globally competitive scenario. Various inventory models are proposed, significant parameters are analysed and finally optimized by researchers in order to give managers an insight for the different parameters. Mathematical and logical analysis of different inventory and supply chain models helps mangers in overall cost reduction and further higher revenue generation. Members often encounter conflicting interest and unforeseen scenario. So, all this make supply chain very complex and dynamic process. Complex and uncertain nature of inventory and supply chain, many times either it is not feasible to solve the issue with traditional methods or it is not cost effective. Thus many researchers are using artificial intelligence approach for investigation. Genetic algorithm is one among them that works efficiently with complex nature of the inventory and supply chain management. This article provides an up to date review about the role of GA in overall inventory and supply chain management.
{"title":"Genetic Algorithm Approach for Inventory and Supply Chain Management","authors":"Poonam Mishra","doi":"10.4018/978-1-7998-8048-6.ch057","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch057","url":null,"abstract":"Inventory and supply chain management is a real concern for business community in today's globally competitive scenario. Various inventory models are proposed, significant parameters are analysed and finally optimized by researchers in order to give managers an insight for the different parameters. Mathematical and logical analysis of different inventory and supply chain models helps mangers in overall cost reduction and further higher revenue generation. Members often encounter conflicting interest and unforeseen scenario. So, all this make supply chain very complex and dynamic process. Complex and uncertain nature of inventory and supply chain, many times either it is not feasible to solve the issue with traditional methods or it is not cost effective. Thus many researchers are using artificial intelligence approach for investigation. Genetic algorithm is one among them that works efficiently with complex nature of the inventory and supply chain management. This article provides an up to date review about the role of GA in overall inventory and supply chain management.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch060
Míriam Born, D. Adamatti, Marilton Sanchotene de Aguiar, Weslen Schiavon de Souza
Nowadays, urban mobility and air quality issues are prominent, due to the heavy traffic of vehicles and the emission of pollutants dissipated in the atmosphere. In the literature, a model of optimal control of traffic lights using Genetic Algorithms (GA) has been proposed. These algorithms have been introduced in the context of control traffic. In order to search for possible solutions to the problems of traffic lights in major urban centers. Thus, the study of the dispersion of pollutants and Genetic Algorithms with simulations performed in Urban Mobility Simulator SUMO (Simulation of Urban Mobility), seek satisfactory solutions to such problems. The AG uses the crossing of chromosomes, in this case the times of the traffic lights, featuring the finest green light times and the sum of each of the pollutants each simulation cycle. The simulations were performed and the results compared analyzes showed that the use of the genetic algorithm is very promising in this context.
目前，城市交通和空气质量问题十分突出，这主要是由于车辆的大量通行和污染物的排放在大气中消散。在文献中，提出了一种基于遗传算法的交通信号灯最优控制模型。这些算法是在控制流量的背景下介绍的。为了寻找主要城市中心交通信号灯问题的可能解决方案。因此，研究污染物的扩散和遗传算法，并在城市移动模拟器SUMO (Simulation of Urban Mobility)中进行仿真，寻求令人满意的解决方案。AG使用染色体的交叉，在这种情况下是交通灯的时间，具有最好的绿灯时间和每个模拟周期中每种污染物的总和。通过对仿真结果的比较分析表明，遗传算法在这种情况下的应用是很有前途的。
{"title":"Use SUMO Simulator for the Determination of Light Times in Order to Reduce Pollution","authors":"Míriam Born, D. Adamatti, Marilton Sanchotene de Aguiar, Weslen Schiavon de Souza","doi":"10.4018/978-1-7998-8048-6.ch060","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch060","url":null,"abstract":"Nowadays, urban mobility and air quality issues are prominent, due to the heavy traffic of vehicles and the emission of pollutants dissipated in the atmosphere. In the literature, a model of optimal control of traffic lights using Genetic Algorithms (GA) has been proposed. These algorithms have been introduced in the context of control traffic. In order to search for possible solutions to the problems of traffic lights in major urban centers. Thus, the study of the dispersion of pollutants and Genetic Algorithms with simulations performed in Urban Mobility Simulator SUMO (Simulation of Urban Mobility), seek satisfactory solutions to such problems. The AG uses the crossing of chromosomes, in this case the times of the traffic lights, featuring the finest green light times and the sum of each of the pollutants each simulation cycle. The simulations were performed and the results compared analyzes showed that the use of the genetic algorithm is very promising in this context.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133109028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch031
S. Jujjavarapu, B. Singh
Before starting semi-pilot/pilot production plants for biochemical metabolites production, it is essential to optimize the fermentation media. This chapter discusses the classical and advanced techniques of media optimization. The statistical approaches save experimental time for developing processing and improving quality. Recent years have seen the growth of integrated approaches of microbial cultures. Optimization techniques such as response surface methodology, artificial neural network, genetic algorithms, differential evolution, ant colony optimization, etc. have received attention recently because of their major applications in various fields. Controlled release formulations have so many versatile applications in the field of pharmaceutical drugs that they have become important tools to apply the modern concept of therapeutic treatment. Process optimization of such formulations, mathematical modelling can play an important role. This chapter discusses various methodologies for optimization of formulation conditions for drug delivery.
{"title":"Optimization Techniques Applications in Biochemical Engineering and Controlled Drug Delivery","authors":"S. Jujjavarapu, B. Singh","doi":"10.4018/978-1-7998-8048-6.ch031","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch031","url":null,"abstract":"Before starting semi-pilot/pilot production plants for biochemical metabolites production, it is essential to optimize the fermentation media. This chapter discusses the classical and advanced techniques of media optimization. The statistical approaches save experimental time for developing processing and improving quality. Recent years have seen the growth of integrated approaches of microbial cultures. Optimization techniques such as response surface methodology, artificial neural network, genetic algorithms, differential evolution, ant colony optimization, etc. have received attention recently because of their major applications in various fields. Controlled release formulations have so many versatile applications in the field of pharmaceutical drugs that they have become important tools to apply the modern concept of therapeutic treatment. Process optimization of such formulations, mathematical modelling can play an important role. This chapter discusses various methodologies for optimization of formulation conditions for drug delivery.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133708777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-8030-0.CH005
A. Recioui
Demand-side management (DSM) is a strategy enabling the power supplying companies to effectively manage the increasing demand for electricity and the quality of the supplied power. The main objectives of DSM programs are to improve the financial performance and customer relations. The idea is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times. The DSM controls the match between the demand and supply of electricity. Another objective of DSM is to maintain the power quality in order to level the load curves. In this chapter, a genetic algorithm is used in conjunction with demand-side management techniques to find the optimal scheduling of energy consumption inside N buildings in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost. The simulations reveal that the adopted strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.
{"title":"Home Load-Side Management in Smart Grids Using Global Optimization","authors":"A. Recioui","doi":"10.4018/978-1-5225-8030-0.CH005","DOIUrl":"https://doi.org/10.4018/978-1-5225-8030-0.CH005","url":null,"abstract":"Demand-side management (DSM) is a strategy enabling the power supplying companies to effectively manage the increasing demand for electricity and the quality of the supplied power. The main objectives of DSM programs are to improve the financial performance and customer relations. The idea is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times. The DSM controls the match between the demand and supply of electricity. Another objective of DSM is to maintain the power quality in order to level the load curves. In this chapter, a genetic algorithm is used in conjunction with demand-side management techniques to find the optimal scheduling of energy consumption inside N buildings in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost. The simulations reveal that the adopted strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117188696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-2375-8.CH009
Rajashree Mishra, K. Das
During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.
{"title":"A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization","authors":"Rajashree Mishra, K. Das","doi":"10.4018/978-1-5225-2375-8.CH009","DOIUrl":"https://doi.org/10.4018/978-1-5225-2375-8.CH009","url":null,"abstract":"During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125786944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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