Study the Effects of Multilevel Selection in Multi-Population Cultural Algorithm

This is a study on the effects of multilevel selection (MLS) theory in optimizing numerical functions. Based on this theory, a new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new multilevel selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory that states natural selection acts collectively on all the members of a given group. The effects of cooperation are studied using n-player prisoner’s dilemma. In this game, N individuals are randomly divided into m groups and individuals independently choose to be either cooperator or defector. A two-level selection process is introduced namely within group selection and between group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. The principal idea behind incorporating this multilevel selection model is to avoid premature convergence and to escape from local optima and for better exploration of the search space. We test our algorithm using the CEC 2015 expensive benchmark functions to evaluate its performance. These problems are a set of 15 functions which includes varied function categories. We show that our proposed algorithm improves solution accuracy and consistency. For 10 dimensional problems, the proposed method has 8 out 15 better results and for 30-dimensional problems we have 11 out of 15 better results when compared to the existing algorithms. The proposed model can be extended to more than two levels of selection and can also include migration

The Role of Prior Knowledge in Multi-Population Cultural Algorithms for Community Detection in Dynamic Social Networks

The relationship between a community and the knowledge that it encompasses is a fundamentally important aspect of any social network. Communities, with some level of similarity, implicitly tend to have some level of similarity in their knowledge as well. This work does the analysis on the role of prior knowledge in Multi-Population Cultural Algorithm (MPCA) for community detection in dynamic social networks. MPCA can be used to find the communities in a social network. The knowledge gained in this process is useful to analyze the communities in other social networks having some level of similarity. Our work assumes that knowledge is an integral part of any community of a social network and plays a very important role in its evolution. Different types of networks with levels of non-similarity are analyzed to see the role of prior knowledge while finding communities in them

DOMINANCE IN MULTI-POPULATION CULTURAL ALGORITHMS

We propose a new approach that can be used for solving the knowledge migration issue in multi-population cultural algorithms (MPCA). In this study we introduce a new method to enable the migration of individuals from one population to another using the concept of complete dominance applied to MPCA. The MPCA’s artificial population comprises of agents that belong to a certain sub-population. In this work we create a dominance multi population cultural algorithm (D-MPCA) with a network of populations that implements a dominance strategy. We hypothesize that the evolutionary advantage of dominance can help improve the performance of MPCA in general optimization problems. Three benchmark optimization functions are used to calculate the fitness value of the individuals. The proposed D-MPCA showed improved performance over the traditional MPCA. We conclude that dominance helps in improving the efficiency of knowledge migration in MPCA

Social Network Analysis using Cultural Algorithms and its Variants

Finding relationships between social entities and discovering the underlying structures of networks are fundamental tasks for analyzing social networks. In recent years, various methods have been suggested to study these networks efficiently, however, due to the dynamic and complex nature that these networks have, a lot of open problems still exist in the field. The aim of this research is to propose an integrated computational model to study the structure and behavior of the complex social network. The focus of this research work is on two major classic problems in the field which are called community detection and link prediction. Moreover, a problem of population adaptation through knowledge migration in real-life social systems has been identified to model and study through the proposed method. To the best of our knowledge, this is the first work in the field which is exploring this concept through this approach. In this research, a new adaptive knowledge-based evolutionary framework is defined to investigate the structure of social networks by adopting a multi-population cultural algorithm. The core of the model is designed based on a unique community-oriented approach to estimate the existence of a relationship between social entities in the network. In each evolutionary cycle, the normative knowledge is shaped through the extraction of the topological knowledge from the structure of the network. This source of knowledge is utilized for the various network analysis tasks such as estimating the quality of relation between social entities, related studies regarding the link prediction, population adaption, and knowledge formation. The main contributions of this work can be summarized in introducing a novel method to define, extract and represent different sources of knowledge from a snapshot of a given network to determine the range of the optimal solution, and building a probability matrix to show the quality of relations between pairs of actors in the system. Introducing a new similarity metric, utilizing the prior knowledge in dynamic social network analysis and study the co-evolution of societies in a case of individual migration are another major contributions of this work. According to the obtained results, utilizing the proposed approach in community detection problem can reduce the search space size by 80%. It also can improve the accuracy of the search process in high dense networks by up to 30% compared with the other well-known methods. Addressing the link prediction problem through the proposed approach also can reach the comparable results with other methods and predict the next state of the system with a notably high accuracy. In addition, the obtained results from the study of population adaption through knowledge migration indicate that population with prior knowledge about an environment can adapt themselves to the new environment faster than the ones who do not have this knowledge if the level of changes between the two environments is less than 25%. Therefore, utilizing this approach in dynamic social network analysis can reduce the search time and space significantly (up to above 90%), if the snapshots of the system are taken when the level of changes in the network structure is within 25%. In summary, the experimental results indicate that this knowledge-based approach is capable of exploring the evolution and structure of the network with the high level of accuracy while it improves the performance by reducing the search space and processing time

Using Heritage in Multi-Population Evolutionary Algorithms

Multi-Population Cultural Algorithms (MPCA) define a set of individuals that can be categorized as belonging to one of a set of populations. Not only reserved for Cultural Algorithms, the concept of Multi-Populations has been used in evolutionary algorithms to explore different search spaces or search for different goals simultaneously, with the capability of sharing knowledge with each other. The populations themselves can define specific goals or knowledge to use in the context of the problem. One limitation of MPCA is that an individual can only belong to one population at a time, which can restrict the potential and realism of the algorithm. This thesis proposes a novel approach to represent population usage called “Heritage,” which allows individuals to belong to multiple populations with weighted influence. Heritage-Dynamic Cultural Algorithm (HDCA) is used to test against different domains to examine the advantages and disadvantages of this approach