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In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are discussed. On the other hand, methods based on protein conformational energy minimization and with different search strategies are analyzed. In this latter case, our methods based on a memetic combination between differential evolution and the fragment replacement technique are included, incorporating also the possibility of niching in the evolutionary search. Different proteins have been used to analyze the pros and cons in both approaches, proposing possibilities of integration of both alternatives. [ABSTRACT FROM AUTHOR]

This study uses different feature selection methods in the field of business failure prediction and tests the capability of Genetic Programming (GP) as an appropriate classifier in this field. The prediction models categorize the insolvency/non-insolvency of a firm one year in advance from a large set of financial ratios. Different selection strategies based on two evolutionary algorithms were used to reduce the dimensionality of the financial features considered. The first method considers the combination between the global search provided by an evolutionary algorithm (differential evolution) with a simple classifier, together with the possible use of classical filters in a first step of feature selection. Secondly, genetic programming is used as a feature selector. In addition, these selection approaches will be compared when GP is used exclusively as a classifier. The results show that, when using GP as a classifier method, the proposed selection method with GP stands out from the rest. Moreover, the use of GP as a classifier improves the results with respect to other classifier methods. This shows an added value to the use of GP in this field, in addition to the interpretability of GP prediction models. [ABSTRACT FROM AUTHOR]

As one of the three basic control parameters of the differential evolution algorithm (DE), the population size (PS) has attracted extensive attention. In general, the most appropriate population size varies for different types of problems and problems with different dimensions. As a result, the performance of an algorithm with a fixed population size is limited to some extent. In this paper, a new enhanced algorithm with a population entropy based population adaptation strategy has been proposed under the framework of SHADE (PE-SHADE). Firstly, a method to calculate the entropy of the population is introduced, through which the distribution state of the population is also characterized. Secondly, the population size is adapted according to the distribution state with a population increasing strategy and a population reduction strategy. In order to evaluate the performance of the proposed algorithm, experiments on the standard benchmark CEC2014 have been conducted, as well as the sensitivity experiments for the extra parameters. The performance comparisons with SHADE, L-SHADE, and some other well-known DE variants are analyzed, which statistically supports the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A hybrid version between differential evolution and the fragment replacement technique was defined for protein structure prediction. The coarse-grained atomic model of the Rosetta system was used for protein representation. The high-dimensional and multimodal nature of protein energy landscapes requires an efficient search for obtaining the native structures with minimum energy. However, the energy model of Rosetta presents an additional difficulty, since the best energy area in the landscape does not necessarily correspond to the closest conformations to the native structure. A strategy is to obtain a diverse set of protein conformations that correspond to different minima in the landscape. The incorporation of the crowding niching method into the hybrid evolutionary algorithm allows addressing the problem of the energy landscape deceptiveness, allowing to obtain a set of optimized and diverse protein folds. [ABSTRACT FROM AUTHOR]

The design of genetic operators is absolutely one of the core work of evolutionary algorithms research. However, the essence of the evolutionary algorithms is that a lot of algorithm design is based on the manual result analysis, summarize, refine, feedback, and then, the algorithms are designed adaptively and correspondingly. This kind of design scheme needs artificial statistics and analysis of large amounts of data, which greatly increases the burden of the designers. To solve this problem, an evolutionary algorithm framework based on genetic operator automatic design is proposed in this paper. In the first step, Gene Expression Programming and Differential Evolution methods are combined together and used to design the genetic operators automatically and adaptively, this hybrid method can not only explore solutions in problem space for the problem solving as most classical evolutionary algorithms do, but also generate genetic operators automatically in operator space for the proper operators extraction and selection related to the evolutionary algorithms. In the second step, the designed operators are adopted into the typical evolutionary algorithms to verify the performance and the result shows that the new designed genetic operator is superior to or at least equivalent to some existing DE variants in a set of classical benchmark functions. More importantly, this paper is not aimed at designing high performance algorithms, but to provide a new perspective for algorithms designing, and to provide a reference scheme for the machine algorithms designing. [ABSTRACT FROM AUTHOR]

A core operator of evolutionary algorithms (EAs) is the mutation. Recently, much attention has been devoted to the study of mutation operators with dynamic and non-uniform mutation rates. Following up on this area of work, we propose a new mutation operator and analyze its performance on the (1 + 1) Evolutionary Algorithm (EA). Our analyses show that this mutation operator competes with pre-existing ones, when used by the (1 + 1) EA on classes of problems for which results on the other mutation operators are available. We show that the (1 + 1) EA using our mutation operator finds a (1/3)-approximation ratio on any non-negative submodular function in polynomial time. We also consider the problem of maximizing a symmetric submodular function under a single matroid constraint and show that the (1 + 1) EA using our operator finds a (1/3)-approximation within polynomial time. This performance matches that of combinatorial local search algorithms specifically designed to solve these problems and outperforms them with constant probability. Finally, we evaluate the performance of the (1 + 1) EA using our operator experimentally by considering two applications: (a) the maximum directed cut problem on real-world graphs of different origins, with up to 6.6 million vertices and 56 million edges and (b) the symmetric mutual information problem using a four month period air pollution data set. In comparison with uniform mutation and a recently proposed dynamic scheme, our operator comes out on top on these instances. [ABSTRACT FROM AUTHOR]

Ensemble learning is a system that combines a set of base learners to improve the performance in machine learning, where accuracy and diversity of base learners are two important factors. However, these two factors are usually contradictory. To address this problem, in this paper, we propose a novel ensemble learning algorithm based on fitness Euclidean-distance ratio differential evolution, to train the neural network ensemble. FEFERR_ELA employs a multimodal evolutionary algorithm that is capable of producing diverse solutions to search for optimal solutions corresponding to parameters of base learners, where each optimal solution leads to one trained model. A dynamic ensemble selection scheme is applied to select appropriate individuals for the ensemble. The proposed algorithm is evaluated on several benchmark problems and compared with some related ensemble learning models. The experimental results demonstrate that the proposed algorithm outperforms the related works and can produce the neural network ensembles with better generalization. [ABSTRACT FROM AUTHOR]

Nature is a great source of inspirations for solving complex computational problems. The inspirations can come from any source like some theory of physics or chemistry, a mathematical concept or from the biological world. Several biologically inspired techniques are implemented in various areas of research and development. These technologies can be grouped into two broad segments: Evolutionary and Swarm based depending on the nature of inspiration. This paper presents an overview of these biologically inspired techniques and its various implementations for traffic optimization with an objective to optimize congestion, minimize wait time, improve safety and reduce pollution. [ABSTRACT FROM AUTHOR]

This paper presents a new ensemble algorithm which combines two well-known algorithms particle swarm optimization (PSO) and differential evolution (DE). To avoid the suboptimal solutions occurring in the previous hybrid algorithms, in this study, an alternative mutation method is developed and embedded in the proposed algorithm. The population of the proposed algorithm consists of two groups which employ two independent updating methods (i.e. velocity updating method from PSO and mutative method from DE). By comparing with the previously generated population at the last generation, two new groups are generated according to the updating methods. Based on the alternative mutation method, the population is updated by the alternative selection according to the evaluation functions. To enhance the diversity of the population, the strategies of re-mutation, crossover, and selection are conducted throughout the optimization process. Each individual conducts the correspondent mutation and crossover strategies according to the parameter values randomly selected, and the parameter values of scaling factor and crossover probability will be updated accordingly throughout the iterations. Numerous simulations on twenty-five benchmark functions have been conducted, which indicates the proposed algorithm outperforms some well-exploited algorithms (i.e. inertia weight PSO, comprehensive learning PSO, and DE) and recently proposed algorithms (i.e. DE with the ensemble of parameters and mutation strategies and ensemble PSO). [ABSTRACT FROM AUTHOR]

A new multi-stage perturbed differential evolution (MPDE) is proposed in this paper. A new mutation strategy "multi-stage perturbation" is implemented with directivity difference information strategy and multiple parameters adaption. The DE/current-to-pbest is introduced to increase the population diversity while remaining its elitist learning behavior in this architecture. The multi-stage perturbation-based mutation operation utilizes the Normal random distribution with adjustable variance to perturb the chosen solutions. Multiple parameters are adaptively adjusted to appropriate values to match the current search status of algorithm. It is thus helpful to enhance the performance and the robustness of algorithm. Simulation results show that the newly proposed MPDE is better than, or at least comparable to CLPSO, SPSO2011, NGHS, jDE, CoDE, SaDE and JADE algorithms in terms of optimization performance based on CEC2015 benchmark function. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient population-based search algorithm for solving numerical optimization problems. However, the performance of DE is very sensitive to the choice of mutation strategies and their associated control parameters. In this paper, we propose a self-adaptive multi-population differential evolution algorithm, called SAMDE. The population is randomly divided into three equally sized sub-populations, each with different mutation strategies. At the end of each generation, all sub-populations are updated independently and recombined. Each sub-population uses an adaptive mechanism for selecting how current generation control parameters are generated. An improved mutation strategy, "rand assemble/1", is proposed, its base vector is composed proportionally of three randomly selected individuals. The performance of SAMDE is evaluated on the suite of CEC 2005 benchmark functions. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that SAMDE has a competitive performance compared to several other efficient DE variants. [ABSTRACT FROM AUTHOR]

Clean energy resources such as wind power are playing an important role in power generation recently. In this paper, a modified version of multi-objective differential evolution (MODE) is used to tackle the extended dynamic economic emission dispatch (DEED) problem by incorporating wind power plant into the system. DEED is a nonlinear and highly constrained multi-objective optimization problem and the predicted load is varying with time. Fuel costs and pollution emission are the two objectives to be optimized and the valve point effect, spinning reserve, real power loss as well as the ramping rate are considered. To solve the model effectively, an ensemble of selection method is used in the MODE algorithm. The real-time output adjustment and penalty factor methods are used to deal with the complex constraints. The proposed method is firstly examined on several multi-objective benchmark problems and the DEED problem without considering the wind power to test its effectiveness of solving multi-objective optimization problems. Secondly, the model considering wind power is solved and the results show that the proposed algorithm is effective in handling such problems. [ABSTRACT FROM AUTHOR]

This paper proposes to model protein folding as an emergent process, using machine learning to infer the folding modeling only from information of known protein structures. Using the face-centered cubic lattice for protein conformation representation, the dynamic nature of protein folding is captured with an evolved neural cellular automaton that defines the amino acids moves along the protein chain and across time. The results of the final folded conformations are compared, using different protein benchmarks, with other methods used in the traditional protein structure prediction problem, highlighting the capabilities and problems found with this modeling. [ABSTRACT FROM AUTHOR]

Canonical particle swarm optimization (PSO) utilizes the historical best experience and neighborhood's best experience of particle through linear summation to guide its search direction. Such a learning strategy is easy to use, but is inefficient when searching in the complex problem space since the global best individual only considers the fitness value but always ignores the diversity information. Hence, designing learning strategies where the guidance exemplar simultaneously considers the fitness value and diversity have become one of the most salient and active PSO research topics. In this paper, a multiobjective sorting-based learning (MSL) strategy for PSO is proposed and this modified PSO is named as multiobjective sorting-based learning particle swarm optimization. The MSL strategy can guide particles to fly in better direction by constructing a guidance exemplar with better fitness value and diversity. Since the fitness value and diversity are simultaneously considered to construct the guidance exemplar instead of the global best individual in canonical PSO, a better balance between exploration and exploitation can be achieved. The proposed strategy is applied to the original PSO algorithm, as well as several advanced PSO variants. Experimental results on sixteen benchmark problems show that the proposed strategy is an effective approach to enhance the performance of most PSO algorithms studied in terms of solution quality, convergence speed and algorithm reliability. [ABSTRACT FROM AUTHOR]

Although Differential Evolution (DE) is a simple yet powerful evolutionary algorithm, it requires an adaptive parameter control to achieve its optimal performance. In this paper, DE with an adaptive parameter control using the $$\alpha$$ -stable distribution is proposed. First, the proposed algorithm allocated a carefully calculated stable distribution, evaluated by an adaptation manner, to each individual. After that, each individual adjusts its own control parameters by using the assigned stable distribution. Thus, we propose a parameter control scheme that adapts the stability parameter of the $$\alpha$$ -stable distribution to allocate proper stable distributions to each individual, used for tuning control parameters. We compared the optimization performances of the proposed algorithm with conventional DE and state-of-the-art DE variants at 30 and 100 dimensions of conventional benchmark problems. Also, we evaluated the optimization performances at high dimensional problems i.e., 100, 200, and 300 dimensions of CEC2008 benchmark problems. Our experiment results showed that the proposed algorithm is able to discover better final solutions than the compared DE algorithms and has the robust performance at both lower and higher dimensions. [ABSTRACT FROM AUTHOR]

Developing efficient algorithms for solving multi-objective optimization problems is a challenging and essential task in many applications. This task involves two or more conflicting objectives that need to be simultaneously optimized. Many real-world problems fall into this category. We introduce an improved version of multi-objective differential evolution (DE) algorithm, namely MO nDE that uses a new mutation strategy and a normalization method to select non-dominated solutions. The new mutation strategy 'DE/rand-to- nbest' uses the best normalized individual in terms of all the objectives to guide the search towards the true pareto optimal solutions. As a result, the probability of producing superior solutions is increased and a faster convergence is achieved. Summation of normalized objective values method is used instead of non-domination sorting to overcome the high computational complexity and overhead problems of sorting non-dominated solutions. The performance of our approach is tested on a set of benchmark problems that consist of two to five objectives. Different combinations of multi-objective evolutionary programming and multi-objective differential evolution algorithms have been used for comparisons. The results affirm the efficiency and robustness of the proposed approach among other well-known algorithms from the literature. [ABSTRACT FROM AUTHOR]

Constrained global optimization is a highly important and challenging task in the field of optimization, and is embedded in many science and engineering optimizations. In this paper, an improved dynamic membrane evolutionary algorithm based on particle swarm optimization and differential evolution (IDMEA-PSO/DE) is proposed to solve constrained engineering design problems. The method combines the dynamic membrane structure of P systems and the PSO/DE search strategy. The performance of IDMEA-PSO/DE is tested on several well-known engineering design problems. The results of the simulation experiments show that the proposed algorithm is effective and outperforms other state-of-the-art-algorithms. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithm is a population based stochastic search technique widely applied in scientific and engineering fields for global optimization over real parameter space. The performance of DE algorithm highly depends on the selection of values of the associated control parameters. Therefore, finding suitable values of control parameters is a challenging task and researchers have already proposed several adaptive and self-adaptive variants of DE. In the paper control parameters are adapted by levy distribution, named as Levy distributed DE (LdDE) which efficiently handles exploration and exploitation dilemma in the search space. In order to assure a fair comparison with existing parameter controlled DE algorithms, we apply the proposed method on number of well-known unimodal, basic and expanded multimodal and hybrid composite benchmark optimization functions having different dimensions. The empirical study shows that the proposed LdDE algorithm exhibits an overall better performance in terms of accuracy and convergence speed compared to five prominent adaptive DE algorithms. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective optimization (EMO) algorithms have been used in various real-world applications. However, most of the Pareto domination based multi-objective optimization evolutionary algorithms are not suitable for many-objective optimization. Recently, EMO algorithm incorporated decision maker's preferences became a new trend for solving many-objective problems and showed a good performance. In this paper, we first use a new selection scheme and an adaptive rank based clone scheme to exploit the dynamic information of the online antibody population. Moreover, a special differential evolution (DE) scheme is combined with directional information by selecting parents for the DE calculation according to the ranks of individuals within a population. So the dominated solutions can learn the information of the non-dominated ones by using directional information. The proposed method has been extensively compared with two-archive algorithm, light beam search non-dominated sorting genetic algorithm II and preference rank immune memory clone selection algorithm over several benchmark multi-objective optimization problems with from two to ten objectives. The experimental results indicate that the proposed algorithm achieves competitive results. [ABSTRACT FROM AUTHOR]

The authors present a set of new measures for comparing optimization algorithms in dynamic environments. The authors introduce a new decomposition based multi-objective genetic algorithm with adaptive multi-population strategy to address multi-objective permutation flowshop scheduling problems. The authors describe a self-adaptive fruit fly optimization algorithm for solving high-dimensional global optimization problems. [Extracted from the article]

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and ℓ1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given. [ABSTRACT FROM AUTHOR]