Development Of Evolutionary Algorithms Research Articles

Learning to Learn Evolutionary Algorithm: A Learnable Differential Evolution

Research on evolutionary optimization has flourished for several decades. Now it has come to a turning point. With the advancement of artificial intelligence, especially deep learning and reinforcement learning, it is becoming appealing to rethink the design and development of evolutionary algorithm (EA). From our perspective, a new-generation EA should be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learned</i> rather than manually designed, based on learning from optimization experiences (such as obtained from optimizing a family of optimization problems), the deep understanding of the roles of recombination operators, and the usage of experiences extracted through history optimization trajectories, so as to intelligently decide the control parameters that can adapt to the problem landscape changes. This learning can be conducted by strongly coupling with reinforcement learning since an evolutionary search procedure can be modeled as a Markov Decision Process (MDP). In this paper we propose a framework for automatic learning of EA, and present an exemplar study on learning a differential evolution (DE). Experimental results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learned</i> adaptive DE is very competitive to some recent EAs on a commonly-used test suite, which indicates that the proposed learning framework has a great potential for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatic</i> design of promising EAs.

A random grouping-based self-regulating artificial bee colony algorithm for interactive feature detection

As the dimensionality and complexity of datasets increase, such as biological data, the demand for fast and efficient technologies to detect relevant feature information from these data is also increasing. Extensive progress has been made in the development of evolutionary algorithms (EAs), which can detect the association between a single feature and class label in high-dimensional data. However, when facing the high-dimensional problem caused by interactions between features, the search space of EAs increases exponentially with an increase in the number of features, resulting in unsatisfactory solution quality. Therefore, this study proposed a random grouping-based self-regulating artificial bee colony algorithm (RCABC). RCABC first decomposes all dataset features into a series of dynamic subsets using a dynamic random grouping (DRG) strategy. Thereafter, a self-regulating bee colony optimizer (S-Optimizer) is used to detect relevant interactive features in each subset. This study demonstrates the superiority of the proposed algorithm through experimental results on synthetic and real-world biological datasets.

An Evolutionary Algorithm: An Enhancement of Binary Tournament Selection for Fish Feed Formulation

Binary tournament (BT) selection is known as an established selection operator that has been employed in various problems. However, in the development of evolutionary algorithms (EA), this selection operator has a drawback in providing an efficient implementation of the union procedure, which cannot guarantee a parsimonious knowledge base with reduced number of rules. Therefore, this paper introduces binary‐standard deviation (SD) tournament selection into EA as an enhancement of BT that can lead to focus on more exploration in terms of searching for the best solutions. The proposed selection operator has been experimented within fish feed formulation in grouper fish farming as a case study on finding the minimum cost and fulfilling constraints. This approach is better than experimental design in terms of costs and time. The motivation for doing so is to search for alternative ingredients for the grouper fish, as the price of trash fish is too luxurious. It is because grouper fish are carnivorous and need many trash fish for better growth. The novelty of the proposed SD tournament selection is compared with BT selection in terms of searching for an efficient but not myopic algorithm. Hence, based on the comparative study, the findings of the enhanced selection operator towards the EA have been convinced and accepted in terms of better cost and fulfilling constraints requirements.

Empirical problem decomposition — the key to the evolutionary effectiveness in solving a large-scale non-binary discrete real-world problem

In this paper, we consider an NP-hard, real-world optimization problem from the field of computer networks. The problem refers to the network survivability and may be considered hard due to its scale. Such problems are usually successfully solved by various Genetic Algorithms (GAs). The recent advances in the development of Evolutionary Algorithms (EAs) dedicated to solving discrete-encoded problems show that GAs employing modern linkage learning techniques seem to have exceptional potential in proposing high-quality results. Many of such GAs are the state-of-the-art methods for various optimization problems, including various domain types and single- and multi-objective optimization problems. These GAs have also been shown effective in solving theoretical and practical problems. Thus, linkage learning development may lead to proposing GAs that are significantly more effective than their predecessors. Therefore, we develop the recently proposed linkage learning technique, namely the linkage learning based on local optimization (3LO) that is (to the best of our knowledge) based on fundamentally different ideas than all other linkage-based problem decomposition techniques. Due to these differences, 3LO has some exceptional features — it is proven that it will never commit some of the possible problem decomposition inconsistencies. However, 3LO was only proposed for binary-encoded problems. Therefore, to fill this gap and adjust 3LO to the considered problem, we propose linkage learning based on local optimization for discrete non-binary search spaces (3LO-nb). The obtained results show that 3LO-nb allows for reaching excellent results. Additionally, our proposition is generic, and its effectiveness may be verified on other non-binary discrete problems.

Development of evolutionary algorithms in the problems of managing the assortment of network pharmacies

The article explores the problems and existing methods of optimizing the financial performance of network pharmacies. Proposed a genetic method with the modification of the mutation operator to solve the problem of managing the range of pharmaceutical products based on evolutionary methods. The fundamental difference between the developed genetic algorithm and its existing counterparts is the ability to control the mathematical distribution of the values of the solution, which prevents premature convergence of the genetic algorithm and uses all proposed genes in fractions according to the chosen distribution model. In addition, three modifications to the genetic operator of mutations are proposed. The first modification is to choose the substitution values for a mutation not in a random way, but from a series, that obeys the law of normal distribution. The second modification serves to determine the appropriateness of the chromosome mutation, based on knowledge of retrospective and predictive data using as a predictive model of SNM. The third modification is the combination of the two modifications mentioned. The application of the developed methods will lead to a more effective use of the pharmacy area, to reduce unmet demand and, ultimately, to reduce the retail cost of drugs by reducing the costs of storing and servicing the suboptimal loading of the pharmacy

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

The interests in multiobjective and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multiobjective and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multiobjective and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms (EAs) for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multiobjective and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multiobjective and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and nonuniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multiobjective and many-objective EAs are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new EAs dedicated to large-scale multiobjective and many-objective optimization.

How to Read Many-Objective Solution Sets in Parallel Coordinates [Educational Forum

Rapid development of evolutionary algor ithms in handling many-objective optimization problems requires viable methods of visualizing a high-dimensional solution set. The parallel coordinates plot which scales well to high-dimensional data is such a method, and has been frequently used in evolutionary many-objective optimization. However, the parallel coordinates plot is not as straightforward as the classic scatter plot to present the information contained in a solution set. In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives. We hope that these observations could provide some guidelines as to the proper use of the parallel coordinates plot in evolutionary manyobjective optimization.

Application of Artificial Bee Colony Algorithm for Distribution Network Reconfiguration

—Network reconfiguration is a process of changing the original structure of the distribution network system with the intention of balancing the load in every system’s feeder at the same time to optimize the operation of the system. The process involve the changing of switching state (tie switches and sectionalize switches), with the aim to find the best combination that can increase the performance of the system while satisfying with the operational constraints. The extreme necessity to the process has become a challenging mission for the researcher to overcome the reconfiguration problems. Recent years have seen a rapid development of evolutionary algorithms and swarm intelligence based algorithms to resolve for network reconfiguration problems. For that reason, this report deals with Artificial Bee Colony (ABC) algorithm to be implemented in network reconfiguration procedure to achieve the optimum level of operation. The ease and simplicity of the algorithm and the capability to find the global optimization solution has made this algorithm appropriate for this project. The objective of this work focused on improvements of distribution power system, in terms of minimizing the total real power loss and improving the voltage profile within the acceptable value. The algorithm was tested on two different radial distribution system (33 bus and 69 bus radial distribution systems)

Evolutionary-Algorithm-Assisted Joint Channel Estimation and Turbo Multiuser Detection/Decoding for OFDM/SDMA

The development of evolutionary algorithms (EAs), such as genetic algorithms (GAs), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), and differential evolution algorithms (DEAs), have stimulated wide interests in the communication research community. However, the quantitative performance-versus-complexity comparison of GA, RWBS, PSO, and DEA techniques applied to the joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding in the context of orthogonal frequency-division multiplexing/space-division multiple-access systems is a challenging problem, which has to consider both the CE problem formulated over a continuous search space and the MUD optimization problem defined over a discrete search space. We investigate the capability of the GA, RWBS, PSO, and DEA to achieve optimal solutions at an affordable complexity in this challenging application. Our study demonstrates that the EA-assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramer-Rao lower bound of the optimal CE and the bit error ratio (BER) performance of the idealized optimal maximum-likelihood (ML) turbo MUD/decoder associated with perfect channel state information, respectively, despite imposing only a fraction of the idealized turbo ML-MUD/decoder's complexity.

Use of the “Sheme Theory” for decision of tasks of allocation of material resources

The history of development of evolutionary algorithms totals more than twenty years. Was developed hundreds methods and thousand algorithms, but this theme still will be long to remain inexhaustible. And in this article is considered using by genetic algorithm of the theory “sheme” for allocation of material resources, and also possibility of forming of minimum requirements in relation to the expense parameters of projects