Evolutionary Computation Techniques Research Articles

Evolutionary computing applied to customer relationship management: A survey

Customer relationship management (CRM) is a customer-centric business strategy which a company employs to improve customer experience and satisfaction by customizing products and services to customers' needs. This strategy, when implemented in totality eventually increases the revenue of the company. Traditionally, data mining (DM) techniques have been applied to solve various analytical CRM tasks. In turn, optimization techniques have long been used for training some of the DM techniques. However, during the past few years, evolutionary techniques have become so powerful and versatile that they can be deployed as a substitute for some DM techniques. This trend caught the attention of the researchers working in the analytical CRM area as they too started solving the CRM tasks using evolutionary techniques alone. In this context, we present a survey of evolutionary computing techniques applied to CRM tasks. In this paper, we surveyed 78 papers that were published during 1998 and 2015, where the application of evolutionary computing (EC) techniques to analytical CRM tasks is the main focus. The survey includes papers involving evolutionary computing techniques applied to the analytical CRM tasks under single- as well as multi-objective optimization framework. The purpose of the survey is to let the reader realize the versatility and power of EC techniques in solving analytical CRM tasks in the service industry and suggesting future directions.

Mining high-utility itemsets based on particle swarm optimization

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of the designed algorithms have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPEumu-GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approach. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm namely HUIM-BPSOsig is proposed to efficiently find HUIs. It first sets the number of discovered high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of a particle based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles of the designed HUIM-BPSOsig algorithm. Substantial experiments on real-life datasets show that the proposed algorithm has better results compared to the state-of-the-art GA-based algorithm.

A Survey on Evolutionary Computation: Methods and Their Applications in Engineering

Evolutionary computation is now an inseparable branch of artificial intelligence and smart methods based on evolutional algorithms aimed at solving different real world problems by natural procedures involving living creatures. It’s based on random methods, regeneration of data, choosing by changing or replacing data within a system such as personal computer (PC), cloud, or any other data center. This paper briefly studies different evolutionary computation techniques used in some applications specifically image processing, cloud computing and grid computing. These methods are generally categorized as evolutionary algorithms and swarm intelligence. Each of these subfields contains a variety of algorithms and techniques which are presented with their applications. This work tries to demonstrate the benefits of the field by presenting the real world applications of these methods implemented already. Among these applications is cloud computing scheduling problem improved by genetic algorithms, ant colony optimization, and bees algorithm. Some other applications are improvement of grid load balancing, image processing, improved bi-objective dynamic cell formation problem, robust machine cells for dynamic part production, integrated mixed-integer linear programming, robotic applications, and power control in wind turbines.

Smart Power Evolution of Food Refrigerated based on Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) algorithm is a new evolutionary computation technique. This article has conducted the thorough research to the PSO algorithm, analyses the algorithm principle, basic steps, application steps and parameter settings and other content. This study introduces the idea of random inertia weight and the discrete PSO algorithm is improved, which makes it easy to jump out of local optima and discrete optimization problem. The establishment of the best repair path and the best routing model of electric power communication network, the improved algorithm is applied to power communication network management in the two aspects, it can quickly find the optimal path and the routing request, prove the feasibility and effectiveness of PSO algorithm for solving the discrete problem. The establishment of the optimal route choice model, based on local search and memory unit in the algorithm, the optimal route selection program using the improved PSO algorithm simulation, the results show the effectiveness of the PSO algorithm for solving the discrete problem and potential.

A Survey on Evolutionary Computation Approaches to Feature Selection

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

Nested hybrid evolutionary model for traffic signal optimization

A noble Nested Hybrid Evolutionary Model is presented to reduce the wait time of vehicles at traffic signals and improve the mobility within the road network. In effect, it contributes towards achieving green environment and reducing the fuel consumption. The proposed model is based on Bi-level Stackelberg Game in which the upper layer is "traffic signals" which is optimized using evolutionary computational techniques (ACO, GA and a Hybrid of ACO and GA) and the lower layer is "stochastic user equilibrium" for which road network is designed using Petri Net (PN) respectively. A comparative analysis has been carried out and it was found that nested hybrid model outperforms ACO and GA.

MODC: Multi-Objective Distance based Optimal Document Clustering by GA

Background/Objective: Unsupervised learning of text documents is an essential and significant process of knowledge discovery and data mining. The concept, context and semantic relevancy are the important and exclusive factors in text mining, where as in the case of unsupervised learning of record structured data, these factors are not in scope. Methods/Statistical Analysis: The current majority of benchmarking document clustering models is keen and relies on term frequency, and all these models are not considering the concept, context and semantic relations during document clustering. In regard to this, our earlier works introduced a novel document clustering approaches and one of that named as Document Clustering by Conceptual, Contextual and Semantic Relevance (DC3SR). The lessons learned from the empirical study of this contribution motivated us to propose aMulti-Objective Distance based optimal document Clustering (MODC) approach that optimizes resultant clusters using the well-known evolutionary computation technique called Genetic Algorithm.Findings: The significant contribution of this proposal is feature formation by concept, context and semantic relevance and optimizing resultant clusters by genetic algorithm. An unsupervised learning approach to form the initial clusters that estimates similarity between any two documents by concept, context and semantic relevance score and further optimizes by genetic algorithm is proposed. This novel method represents the concept as correlation between arguments and activities in given documents, context as correlation between meta-text of the documents and the semantic relevance is assessed by estimating the similarity between documents through the hyponyms of the arguments. The meta-text of the documents considered for context assessment contains the authors list, keywords list and list of document versioning time schedules. Application/Improvements:The experiments were conducted to assess the significance of the proposed model.The results obtained from experiments concluding that the MODC is performing exceptionally well under divergent document count and evincing the cluster formation accuracy as 97%. The dimensionality reduction by concept, context and semantic relevance is left for future enhancement of the proposed model.

Quantum seeded evolutionary computational technique for constrained optimization in engineering design and manufacturing

In this paper an attempt is made to develop a new Quantum Seeded Hybrid Evolutionary Computational Technique (QSHECT) that is general, flexible and efficient in solving single objective constrained optimization problems. It generates initial parents using quantum seeds. It is here that QSHECT incorporates ideas from the principles of quantum computation and integrates them in the current framework of Real Coded Evolutionary Algorithm (RCEA). It also incorporates Simulated Annealing (SA) in the selection process of Evolutionary Algorithm (EA) for child generation. The proposed algorithm has been tested on standard test problems and engineering design problems taken from the literature. In order to test this algorithm on domain-specific manufacturing problems, Neuro-Fuzzy (NF) modeling of hot extrusion is attempted and the NF model is incorporated as a fitness evaluator inside the QSHECT to form a new variant of this technique, i.e. Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Computational Technique (QSNFHECT) and is effectively applied for process optimization of hot extrusion process. The neuro-fuzzy model (NF) is also compared with statistical regression analysis (RA) model for evaluating the extrusion load. The NF model was found to be much superior. The optimal process parameters obtained by Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Technique (QSNFHECT) are validated by the finite element model. The proposed methodology using QSNFHECT is a step towards meeting the challenges posed in intelligent manufacturing systems and opens new avenues for parameter estimation and optimization and can be easily incorporated in existing manufacturing setup.

Discrete Fireworks Algorithm for Single Machine Scheduling Problems

Over the recent years, Fireworks Algorithm has recorded an increasing success on solving continuous optimization problems, due to its efficiency, simplicity and more importantly its rapid convergence to good optimums. Thus, the Fireworks Algorithm performance is now widely comparable with the most popular methods in the optimization field such as evolutionary computation and swarm intelligence techniques. This paper introduces a discrete Fireworks Algorithm for combinatorial single machine scheduling problems. Taking advantage of the robust design of the original Fireworks Algorithm, a new adaptation of sparks generation is proposed with a novel use of the control parameters. To verify the explorative performance of the algorithm, a hybridization with Variable Neighborhood Search heuristic is implemented. To validate it, the proposed method is tested with several benchmarks instances of the single machine total weighted tardiness. A comparison with other optimization algorithms is also included. The obtained results exhibit the high performance of the proposed method.

Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons.

The way in which a neuronal tree expands plays an important role in its functional and computational characteristics. We aimed to study the existence of an optimal neuronal design for different types of cortical GABAergic neurons. To do this, we hypothesized that both the axonal and dendritic trees of individual neurons optimize brain connectivity in terms of wiring length. We took the branching points of real three-dimensional neuronal reconstructions of the axonal and dendritic trees of different types of cortical interneurons and searched for the minimal wiring arborization structure that respects the branching points. We compared the minimal wiring arborization with real axonal and dendritic trees. We tested this optimization problem using a new approach based on graph theory and evolutionary computation techniques. We concluded that neuronal wiring is near-optimal in most of the tested neurons, although the wiring length of dendritic trees is generally nearer to the optimum. Therefore, wiring economy is related to the way in which neuronal arborizations grow irrespective of the marked differences in the morphology of the examined interneurons.Electronic Supplementary MaterialThe online version of this article (doi:10.1007/s12021-016-9309-6) contains supplementary material, which is available to authorized users.

Delineating zones to increase geographical detail in individual response data files: An application to the Spanish 2011 Census of population

Abstract Due to confidentiality considerations, the microdata available from the 2011 Spanish Census have been codified at a provincial (NUTS 3) level except when the municipal (LAU 2) population exceeds 20,000 inhabitants (a requirement that is met by less than 5% of all municipalities). For the remainder of the municipalities within a given province, information is only provided for their classification in wide population intervals. These limitations, hampering territorially-focused socio-economic analyses, and more specifically, those related to the labour market, are observed in many other countries. This article proposes and demonstrates an automatic procedure aimed at delineating a set of areas that meet such population requirements and that may be used to re-codify the geographic reference in these cases, thereby increasing the territorial detail at which individual information is available. The method aggregates municipalities into clusters based on the optimisation of a relevant objective function subject to a number of statistical constraints, and is implemented using evolutionary computation techniques. Clusters are defined to fit outer boundaries at the level of labour market areas.

Metaheuristic-based simulation optimization approach to network revenue management with an improved self-adjusting bid price function

ABSTRACTMaking accurate accept/reject decisions on dynamically arriving customer requests for different combinations of resources is a challenging task under uncertainty of competitors' pricing strategies. Because customer demand may be affected by a competitor's pricing action, changes in customer interarrival times should also be considered in capacity control procedures. In this article, a simulation model is developed for a bid price–based capacity control problem of an airline network revenue management system by considering the uncertain nature of booking cancellations and competitors' pricing strategy. An improved bid price function is proposed by considering competitors' different pricing scenarios that occur with different probabilities and their effects on the customers' demands. The classical deterministic linear program (DLP) is reformulated to determine the initial base bid prices that are utilized as control parameters in the proposed self-adjusting bid price function. Furthermore, a simulation optimization approach is applied in order to determine the appropriate values of the coefficients in the bid price function. Different evolutionary computation techniques such as differential evolution (DE), particle swarm optimization (PSO), and seeker optimization algorithm (SOA), are utilized to determine these coefficients along with comparisons. The computational experiments show that promising results can be obtained by making use of the proposed metaheuristic-based simulation optimization approach.

Computational model for predicting the effect of process parameters on surface characteristics of mass finished components

Purpose Mass finishing is a commonly employed surface finishing process for improving surface characteristics of aerospace engineering components. Optimization of surface characteristics of such critical components require an explicit computational model that can describe the surface characteristics of the finished component. This paper aims to develop an explicit computational model that can describe the surface roughness as a function of various process parameters which influence the mass finishing process. Design/methodology/approach In the present work, the authors propose to study the roughness characteristics using a combined evolutionary computing approach based on Multi-Adaptive Regression Splines (MARS) and Genetic Programming (GP) techniques. Findings The authors conducted sensitivity and parametric analysis to capture the dynamics of surface characteristics by unveiling dominant input variables and hidden non-linear relationships. It is found that by regulating the process time and media size, a greatest variation in surface finish reduction can be achieved in mass finishing process. Originality/value To the best of authors knowledge, for the first time a hybrid evolutionary computational technique has been proposed in this work. The authors combined two powerful evolutionary techniques, namely Multi-variate Adaptive Regressive Splines and Genetic Programming approach. The proposed approach was able to capture the dynamics of surface roughness with higher accuracy as comparable to that of the experiments.

Speed Control of Micro Gas Turbine with PMSG using Evolutionary Computational Techniques

This paper studies speed control of Micro Gas Turbine with PD controller. The article investigate the behavior of Micro Gas turbine with PMSG under load variations using different controllers .Most of these controllers are based on Evolutionary Computational Techniques (ECT), among these Evolutionary Computational Techniques, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Adaptive Accelerated Coefficients Particle Swarm Optimization (AACPSO). In this study, PD parameters are defined using a conventional PD controller is tuned by Ziegler-Nicholas technique. The conventional controller compared with (ECT). Simulation results show that the response of the PSO-Fuzzy like PD, AACPSO-Fuzzy like PD, and AACPSO- PD controller is effectively improved compared with other controllers. The effectiveness of the proposed scheme is confirmed via extensive study using MATLAB-Simulink software. The obtained results are promising.

A Hybrid Clustering Technique Combining A PSO Algorithm with K-Means

Particle Swarm Optimization (PSO) is an evolutionary computation technique. Separate adjustment to inertia weight and learning factors in PSO undermines the integrity and intelligent characteristic in the evolutionary process of particle swarm to some extent, thus it is not suitable for solving most complicated optimization problems. On the basis of previous researches, the aim of this study was to improve the computational efficiency of PSO and avoid premature convergence for multimodal, higher dimensional complicated optimization problems by considering the mutual influences of inertia weight and learning factors on the updates of particle’s velocities. A typical data analytical scenario is a multidimensional problem and data clustering can lead to multi spatial analysis. Cluster can be a result of various algorithms. In this paper PSO based k-means clustering is applied to generate clusters. And provide multimodal and higher dimensional complicated optimization problems, and can accelerate convergence speed, improve optimization quality effectively in comparison to the algorithms of PSO Kmeans.

Flower Pollination Algorithm Optimized PI-PD Cascade Controller in Automatic Generation Control of a Multi-area Power System

This article presents automatic generation control (AGC) of an interconnected four area thermal system. The control areas are provided with single reheat turbine and generation rate constraints of 3%/min. A maiden attempt has been made to apply a Proportional integral-Proportional derivative (PI-PD) cascade controller in AGC. Controller gains are optimized simultaneously using Flower Pollination Algorithm (FPA), a recent evolutionary computational technique. Performance of classical controllers such as Integral (I), Proportional Integral (PI) and Proportional Integral Derivative (PID) controller are investigated and compared with PI-PD cascade controller. Investigations reveal that in this comparison PI-PD cascade controller provides much better response than others. The performances comparison of several objective functions are evaluated and explored that integral squared error is better than others for the system with the PI-PD cascade controller. Sensitivity analysis reveals that the FPA optimized PI-PD cascade controller parameters obtained at nominal condition of loading, size, position of disturbance and system parameter such as inertia constant, H are robust and need not be reset with wide changes in system loading, size, position of disturbance and system parameters. The system dynamic performances are studied with 1% step load perturbation, random load in Area 1.

A binary PSO approach to mine high-utility itemsets

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of them have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPE$$ _\mathrm{umu}$$umu-GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approaches. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm, namely HUIM-BPSO, is proposed to efficiently find HUIs. The designed HUIM-BPSO algorithm finds the high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of the particles based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles for the designed HUIM-BPSO algorithm. An OR/NOR-tree structure is further developed to reduce the invalid combinations for discovering HUIs. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of execution time, number of discovered HUIs, and convergence.

Study of Consumers\' Acceptance of Eco-friendly Green Food Hotels

This study is different from previous literature, as it uses AI technology, such as the artificial neural network and evolutionary computation techniques to analyze Taiwan's consumers' acceptance of eco-friendly green food hotels by sensor data mining, in order to know the characteristics of the consumption intentions of respondents. First, Grey Relational Analysis (GRA) is used to discuss the acceptance of various domains in the questionnaire and then the GRA performance values of the various domains are used for clustering analysis, in order to know the characteristics of the consumption intentions of different respondents. Finally, the evolutionary computation technique is combined with the artificial neural network to build an eco-friendly hotel acceptance detection model as reference for eco-friendly hotel operators. The findings show that, in the hotel operators' view, increasing the room rate of eco-friendly green food hotels can be accepted by consumers; the consumers agree with the subject of resource recovery; government bodies shall subsidize hotel operators in promoting eco-friendly green food hotels, thus, contributing to the promotion of eco-friendly green food hotels.

Evolutionary computing in recommender systems: a review of recent research

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.

Modeling and Simulation for High Energy Sub-Nuclear Interactions Using Evolutionary Computation Technique

High energy sub-nuclear interactions are a good tool to dive deeply in the core of the particles to recognize their structures and the forces governed. The current article focuses on using one of the evolutionary computation techniques, the so-called genetic programming (GP), to model the hadron nucleus (h-A) interactions through discovering functions. In this article, GP is used to simulate the rapidity distribution of total charged, positive and negative pions for p--Ar and p--Xe interactions at 200 GeV/c and charged particles for p-pb collision at 5.02 TeV. We have done so many runs to select the best runs of the GP program and finally obtained the rapidity distribution as a function of the lab momentum , mass number (A) and the number of particles per unit solid angle (Y). In all cases studied, we compared our seven discovered functions produced by GP technique with the corresponding experimental data and the excellent matching was so clear.