Dynamic Evolutionary Algorithm Research Articles

An Effective Dynamic Evolutionary Algorithm for Engineering Problems

A dynamic evolutionary algorithms (DEA) is designed to solve engineering problems in this paper. The DEA algorithm makes two differences. (1) Dynamic technique is used to handle equality constraints. (2) Two unrelated crossovers (linear crossover and uniform crossover) are combined in the algorithm for avoiding duplicate search and then helping global search. In solving engineering problems, three steps are taken: a DEA algorithm is designed first, then after tested by general benchmark problems, it is improved, and the third step is that the improved DEA algorithm is applied to solve engineering problems. The general test suggests our DEA algorithm outperforms the compared state-of-the-art other algorithms. The experimental results in solving 5 engineering problems indicate that our method works much better than the compared state-of-the-art algorithms, especially, in global search.

A Dynamic Evolutionary Algorithm for Automated Antenna Design

A Dynamic Evolutionary Algorithm for Automated Antenna Design

Achieving balance between proximity and diversity in multi-objective evolutionary algorithm

Currently, an alternative framework using the hypervolume indicator to guide the search for elite solutions of a multi-objective problem is studied in the evolutionary multi-objective optimization community very actively, comparing to the traditional Pareto dominance based approach. In this paper, we present a dynamic neighborhood multi-objective evolutionary algorithm based on hypervolume indicator (DNMOEA/HI), which benefits from both Pareto dominance and hypervolume indicator based frameworks. DNMOEA/HI is featured by the employment of hypervolume indicator as a truncation operator to prune the exceeded population, while a well-designed density estimator (i.e., tree neighborhood density) is combined with the Pareto strength value to perform fitness assignment. Moreover, a novel algorithm is proposed to directly evaluate the hypervolume contribution of a single individual. The performance of DNMOEA/HI is verified on a comprehensive benchmark suite, in comparison with six other multi-objective evolutionary algorithms. Experimental results demonstrate the efficiency of our proposed algorithm. Solutions obtained by DNMOEA/HI well approach the Pareto optimal front and are evenly distributed over the front, simultaneously.

A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization

A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimizationA novel, neural network controlled, dynamic evolutionary algorithm is proposed for the purposes of molecular geometry optimization. The approach is tested for selected model molecules and some molecular systems of importance in biochemistry. The new algorithm is shown to compare favorably with the standard, statically parametrized memetic algorithm.

Dynamic Evolutionary Algorithm With Variable Relocation

Many real-world optimization problems have to be solved under the presence of uncertainties. A significant number of these uncertainty problems falls into the dynamic optimization category in which the fitness function varies through time. For this class of problems, an evolutionary algorithm is expected to perform satisfactorily in spite of different degrees and frequencies of change in the fitness landscape. In addition, the dynamic evolutionary algorithm should warrant an acceptable performance improvement to justify the additional computational cost. Effective reuse of previous evolutionary information is a must as it facilitates a faster convergence after a change has occurred. This paper proposes a new dynamic evolutionary algorithm that uses variable relocation to adapt already converged or currently evolving individuals to the new environmental condition. The proposed algorithm relocates those individuals based on their change in function value due to the change in the environment and the average sensitivities of their decision variables to the corresponding change in the objective space. The relocation occurs during the transient stage of the evolutionary process, and the algorithm reuses as much information as possible from the previous evolutionary history. As a result, the algorithm shows improved adaptation and convergence. The newly adapted population is shown to be fitter to the new environment than the original or most randomly generated population. The algorithm has been tested by several dynamic benchmark problems and has shown competitive results compared to some chosen state-of-the-art dynamic evolutionary approaches.

On the analysis of a dynamic evolutionary algorithm

Evolutionary algorithms are applied as problem-independent optimization algorithms. They are quite efficient in many situations. However, it is difficult to analyze even the behavior of simple variants of evolutionary algorithms like the ( 1 + 1 ) EA on rather simple functions. Nevertheless, only the analysis of the expected run time and the success probability within a given number of steps can guide the choice of the free parameters of the algorithms. Here static ( 1 + 1 ) EAs with a fixed mutation probability are compared with dynamic ( 1 + 1 ) EAs with a simple schedule for the variation of the mutation probability. The dynamic variant is first analyzed for functions typically chosen as example-functions for evolutionary algorithms. Afterwards, it is shown that it can be essential to choose the suitable variant of the ( 1 + 1 ) EA. More precisely, functions are presented where each static ( 1 + 1 ) EA has exponential expected run time while the dynamic variant has polynomial expected run time. For other functions it is shown that the dynamic ( 1 + 1 ) EA has exponential expected run time while a static ( 1 + 1 ) EA with a good choice of the mutation probability has polynomial run time with overwhelming probability.

The dynamic evolutionary modeling of HODEs for time series prediction

The prediction of future values of a time series generated by a chaotic dynamic system is an extremely challenging task. Besides some methods used in traditional time series analysis, a number of nonlinear prediction methods have been developed for time series prediction, especially the evolutionary algorithms. Many researchers have built various models by utilizing different evolutionary techniques. Different from those available models, this paper presents a new idea for modeling time series using higher-order ordinary differential equations (HODEs) models. Accordingly, a dynamic hybrid evolutionary modeling algorithm called DHEMA is proposed to approach this task. Its main idea is to embed a genetic algorithm (GA) into genetic programming (GP) where GP is employed to optimize the structure of a model, while a GA is employed to optimize its parameters. By running the DHEMA, the modeling and predicting processes can be carried on successively and dynamically with the renewing of observed data. Two practical examples are used to examine the effectiveness of the algorithm in performing the prediction task of time series whose experimental results are compared with those of standard GP.

Theoretical analysis of mutation-adaptive evolutionary algorithms.

Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.