Benchmarking surrogate-assisted genetic recommender systems

Surrogate models of fitness have been presented as a way of reducing the number of fitness evaluations required by an evolutionary algorithm. This is of particular interest with expensive fitness functions where the cost of building the model is outweighed by the saving of using fewer function evaluations. In this paper we show how a Markov network model can be used as a surrogate fitness function in a genetic algorithm. We demonstrate this applied to a number of well-known benchmark functions and although the results are good in terms of function evaluations the model-building overhead requires a substantially more expensive fitness function to be worthwhile. We move on to describe a fitness function for feature selection in Case-Based Reasoning, which is considerably more expensive than the other benchmark functions we used. We show that for this problem using the surrogate offers a significant decrease in total run time compared to a GA using the true fitness function.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Today, advertisement has become a part of media content and it is exposed to viewers in ways such as home shopping, PPL (Product Placement), and T-commerce. Even though media contents and platforms have been developed into user-interactive services, the interaction between advertisement systems and users is still limited. For advertising services to interact with users, a recommendation system should be introduced. Recommendation systems have been studied such as content-based, collaborative filtering, and hybrid methods. Since conventional recommendation systems require high computational complexity in the preference prediction process and do not consider the time factor, there are limitations in performing recommendation considering user preference. In this paper, we propose an advertisement recommendation system that predicts the ATU’s (Active Target User) preference for items in real-time. The proposed system creates PRETREE by modeling ATU's history data with a tree data structure for reducing computational complexity. A recommendation is triggered when the item that the ATU is looking at in the media content is detected. If there are rating records for the item in the history of the ATU, the preferences are predicted on the basis of PRETREE. If there is no history for the item, the proposed system predicts preference on the basis of similar groups and the LCC (Longest Common Category) node at the ATU's PRETREE. Finally, we evaluate the performance of the proposed recommendation system using real- world data.

Nowadays, recommender systems are widely implemented in E-commerce websites to assist customers in finding the items they need. A recommender system should also be able to provide users with useful information about the items that might interest them. The ability of promptly responding to changes in user's preference is a valuable asset for such systems. This paper presents an innovative recommender system for music data that combines two methodologies, the content-based filtering technique and the interactive genetic algorithm. The proposed system aims to effectively adapt and respond to immediate changes in users' preferences. The experiments conducted in an objective manner exhibit that our system is able to recommend items suitable with the subjective favorite of each individual user.

A location-based orientation-aware recommender system using IoT smart devices and Social Networks

The chapter covers two main areas, these being an introduction to the technology and techniques associated with genetic algorithms and then the second part looks at how genetic algorithms can be used to search for good topological solutions to engineering design challenges. The start of the chapter places genetic algorithms in context compared to other evolutionary algorithms and also describes the reasons why genetic algorithms are potentially useful. This is then followed by a look at the concept of a search space. Section two looks at the canonical genetic algorithm as a basic introduction to the technology and includes an examination of the main techniques used to encode the genome, fitness functions, operators and selection. Section three looks at how genetic algorithms can be used for design and chooses the specific example of the conceptual design of commercial office buildings. Section four introduces the basic concepts of topological search and explains how having the right form of representation is vital before looking at example relating to structural components and the design of domes using a genetic algorithm linked to computational geometry techniques. The final section then looks at further methods using generative representations and generative geometries as possible solutions to the need to develop powerful forms of representation for handling topological search in genetic algorithms.

HTSA: A novel hybrid task scheduling algorithm for heterogeneous cloud computing environment

The precision of Information Retrieval (IR) System is low due to imprecise user queries as well as because of information overload on web. The Fuzzy set infers the user’s information need from vague and imprecise queries and web recommender systems are used to overcome information overload problem. The performance of recommender system is still low due to data sparsity. The concept of trust is used to deal with data sparseness problem and improves the performance of recommender system. Optimization techniques like Genetic Algorithm(GA) have been applied in domain of information retrieval for effective web search. In this research hybrid of Fuzzy set, GA and Trust has been used together in query log mining for personalized web search based on using fuzzy queries for recommendation of optimal set of trusted documents. Thus the use of hybrid of Fuzzy set, trust and GA together infer the user’s information need from vague and imprecise user’s queries and optimize the web page ranking of trusted web pages for effective personalized web search. The experimental results were analyzed statistically as well as compared with GA IR, and Fuzzy Trust based IR. Hence based on comparative analysis of results, thus hybrid of Fuzzy Set, Trust and GA shows the improvement in average precision of search results and confirms the effective personalization of web search.

The scheduling problem is a typical time table problem in educational administration. For such a NP complete problems, when the genetic algorithm solves this problem, it has precociousness phenomenon and quickly converges not to the global optimal solution but to the local optimal solution. Therefore, we use the advantage of local search method and partial matching crossover operation to solve TTP problem with the genetic algorithm combining search algorithm. We do a lot of experiments and evaluate the performance of the improved genetic algorithm. The experiment results show that genetic search algorithm is a more superior algorithm to apply to the TTP problem.

Information Gain Clustering through Prototype - Embedded Genetic K-Mean Algorithm (IGCPGKA) is a novel heuristic used in Recommendation System (RS) for solving personalization problems.In a bid to generate information on the behavior and effectiveness of Prototype-Embedded Genetic K-mean Algorithm (PGKA) -- a clustering algorithm - in Recommender System (RS) used in personalization of cold start problem, IGCPGKA is proposed, developed and experimented upon in this work/paper.IGCPGKA is derived from IGCEGA (Information Gain Clustering through Elitizt Genetic Algorithm). The main difference between the two algorithms is articulated and exhibited in the clustering stage, and precisely, PGKA is used in IGCPGKA, while EGA (Elitizt Genetic Algorithm) is used in IGCEGA for clustering purposes. The effects of these differences have created positive results in terms of better recommendation for IGCPGKA and this fact is supported by the two evaluation metrics used in this work, namely Expected Utility (EU) and Mean Absolute Error (MAE).A comparison with other heuristics for personalization of cold start problem - such as Information Gain Clustering Neighbor through Bisecting K-Mean Algorithm (IGCN), Information Gain Clustering through Genetic Algorithm (IGCGA), entropy and popularity -- showed that IGCPGKA emerged vector by producing the best recommendation and this fact is also supported by the two evaluation metrics used in this work.

Deep learning is an artificial intelligence technique that has been used for various tasks. Deep learning performance is determined by its hyperparameter, architecture, and training (connection weight and bias). Finding the right combination of these aspects is very challenging. Convolution neural networks (CNN) is a deep learning method that is commonly used for image classification. It has many hyperparameters; therefore, tuning its hyperparameter is difficult. In this research, a metaheuristic approach is proposed to optimize the hyperparameter of convolution neural networks. Three metaheuristic methods are used in this research: ant colony optimization (ACO), genetic algorithm (GA), and Harmony Search (HS). The metaheuristics methods are used to find the best combination of 8 hyperparameters with 8 options each which creates 1.6. 107 of solution space. The solution space is too large to explore using manual tuning. The Metaheuristics method will bring benefits in terms of finding solutions in the search space more effectively and efficiently. The performance of the metaheuristic methods is evaluated using MNIST datasets. The experiment results show that the accuracy of ACO, GA and HS are 99,7%, 97.7% and 89,9% respectively. The computational times for the ACO, GA and HS algorithms are 27.9 s, 22.3 s, and 56.4 s, respectively. It shows that ACO performs the best among the three algorithms in terms of accuracy, however, its computational time is slightly longer than GA. The results of the experiment reveal that the metaheuristic approach is promising for the hyperparameter tuning of CNN. Future research can be directed toward solving larger problems or improving the metaheuristics operator to improve its performance.

Frequent use of internet applications and rapid increase in volumes of resources have made it difficult for online users to effectively make decisions on the kinds of information or items to select. Recommender systems (RSs) are intelligent decision-support tools that exploit preferences of users and suggest items that might be interesting to them. RSs are one of the various solutions proposed to address the problems of information overload. Traditionally, RSs use single rating techniques to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various attributes of items for improving prediction and recommendation accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed at employing artificial neural networks to model the criteria ratings and determine the predictive performance of the systems based on aggregation function approach. Seven evaluation metrics have been used to evaluate and measure the accuracy of the systems. The empirical results of the study have shown that the proposed technique has the highest prediction and recommendation than the corresponding traditional technique.

It is well known that if a stochastic service system (such as a cellular network) is shared by users with different characteristics (such as differing handoff rates or call holding times), the overall system performance can be improved by denial of service requests even when the success capacity exists. Such selective denial of service based on the system state is defined as the call admission. A previous paper suggested the use of genetic algorithms (GAs) to find near-optimal call admission policies for cellular networks. In this paper, we define local call admission policies that make admission decisions based on partial state information. We search for the best local call admission policies for one-dimensional (1-D) cellular networks using genetic algorithms and show that the performance of the best local policies is comparable to optima for small systems. We test our algorithm on larger systems and show that the local policies found outperform the maximum packing and best handoff reservation policies for the systems we have considered. We find that the local policies suggested by the genetic algorithm search in these cases are double threshold policies. We then find the best double threshold policies by exhaustive search for both 1-D and Manhattan model cellular networks and show that they almost always outperform the best trunk reservation policies for these systems.

Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives