Genetic Optimization via Diverse Crossover Intelligence

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.

In this paper, we propose a new design methodology of fuzzy-neural networks - fuzzy set-based polynomial neural networks (FSPNN) with symbolic genetic algorithms. Fuzzy set-based polynomial neural networks (FSPNN) are based on a fuzzy set-based polynomial neuron (FSPN) whose fuzzy rules include the information granules (about the real system) obtained through information granulation. The information granules are capable of show the specific characteristic of the system. We have developed a design methodology (genetic optimization using symbolic genetic algorithms) to find the optimal structure for fuzzy-neural networks that expanded from group method of data handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FSPNN fixed by aid of symbolic genetic optimization that has search capability to find the optimal solution on the solution space. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. Symbolic genetic algorithms are capable of reducing the solution space more than conventional genetic algorithms with binary genetype chromosomes. The performance of genetically optimized FSPNN (gFSPNN) with aid of symbolic genetic algorithms is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems. However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm.

This paper discusses the integrated topology and sizing optimization of frame structures where beam elements are used in the structural analysis model. Based on the ground structure approach, discrete variables (0, 1) representing the absence or presence of beam members and continuous sizing variables for beam cross sections are involved in the problem. A two-level multipoint approximation strategy is introduced to address this mixed-variable problem. A first-level approximate problem is first constructed using the branched multipoint approximate function involving both discrete and continuous variables. For solving this problem, discrete variables are optimized through a genetic algorithm, and when calculating the fitness in the genetic algorithm, a second-level approximate problem solved by the dual method is established to optimize the cross sections for the retained beam members. Temporal deletion techniques are used for displacement and stress constraints. Meanwhile, by considering local vibration modes in frequency-constrained problems, material density is assigned with an extremely small value for the removed beams to avoid these local modes. Thence, the singularities encountered in stress, displacement, and frequency constraints are overcome. Typical examples and a satellite application are given to verify the feasibility and efficiency of this method in handling the integrated topology and sizing optimization for frame structures.

The metaheuristic genetic algorithm is an evolutionary algorithm which means that it will always evolve to get an optimum solution. Due to this intrinsic characteristic, the conventional genetic algorithm might be trapped at local optimum when dealing with a global optimization problem that consists of several maximum points. As such, this paper aims to explore the potential of improving the genetic algorithm by manipulating its operators. With different procedures in selection and replacement operators, the genetic algorithm is be able to compute more efficiently. This mechanism is introduced to prevent the proposed algorithm to be trapped at the local optimum point with shorter computation time. The robustness of the proposed algorithm is tested in optimizing a wireless sensor network (WSN) because the WSN will exhibit multiple peaks with different network configuration. The existence of multiple peaks will lead to additional difficulties for the conventional routing protocol algorithm in tracking the global optimum network configuration or known as global optima. The simulation results show the effect of proposed genetic algorithm with different combinations of operators.

Optimum design of structures with stress and displacement constraints using the force method

Aimed for the problems in traditional genetic algorithm of low search efficiency and easily falling into the local optimal solution, an improved genetic algorithm is proposed in this paper. So as to save the storage space, the simple one-dimensional code method is adopted to replace the method of complex two-dimensional coding. In the design of genetic operators, many operations such as crossover and mutation are redefined to avoid getting into the local optimum. Then the two fitness functions of collision-free path and the shortest distance are fused into one for the following genetic optimization. In the case of the same population parameters, 100 trials are respectively developed with the method of improved genetic algorithm and traditional genetic algorithm. Among them, the improved genetic algorithm to search the optimal path gets to 95 times, and the shortest path is 20.9706. Besides, the average searching time takes up 217ms. While the number of traditional method to search for the optimal path reaches up to 62 times, the shortest path can be 25.0711, and the average searching time needs 345ms. So compared to the tests results referred above, the improved genetic algorithm is more efficient and can get a better solution than the traditional genetic algorithm.

Genetic algorithms have a problem in premature convergence, which causes the population of prospective solutions to the genetic algorithms to be trapped in suboptimum values. This premature convergence occurs due to a lack of speciation or diversity of the population of prospective solutions. To overcome premature convergence, increasing the diversity of the population of prospective solutions is necessary. Diversity makes genetic algorithms run smoothly to provide recommendations or personalize content in crowdsourcing media monitoring systems. This research attempts to overcome the premature convergence of genetic algorithms by adopting the CRISPR / Cas9 approach. CRISPR / Cas9 is a genome editing method that can edit targeted genes with template genes. The goal is to increase the diversity of the prospective solutions population, hoping that premature convergence does not occur. As a result, CRISPR / Cas9 has proven successful in overcoming premature convergence. However, more processing time and memory are needed than conventional genetic algorithms.

Based on cerebellar model articulation controller (CMAC), this paper attempts to propose a new chromosome representation scheme for representing real number parameters in genetic algorithms (GAs), which is termed CMAC-based GAs. The central idea of CMAC-based GAs is that each memory unit in a CMAC network is regarded as a real-valued gene in GAs. By this way, a chromosome is represented as those memory units addressed by a specific state and each gene involves partial information about a potential solution, not a potential solution as the conventional GAs. In addition, the corresponding crossover and mutation operators are also derived for the proposed GAs. The proposed CMAC-based GAs is applied to optimize the parameters of the proportional-integral-derivative controller. Simulation results are compared with several previous findings to demonstrate the search performance of the proposed method.

The least-cost multicast routing with delay constrained is an NP-Complete problems, to deal with which, a Multi-Agent Immune Clonal Selection Algorithm based multicast routing (MAICSA) is proposed in this paper. MAICSA combines the characteristic of Multi-Agent with the search strategy of Immune Clonal Selection Algorithm. To compare with the conventional Genetic Algorithm (GA), MAICSA overcomes the most serious drawbacks, such as slow convergence rate and “prematurity”. The experimental results show that MAICSA has faster astringency and higher precision than traditional GA, MAGA (Multi-Agent multicast routing based on Genetic Algorithm) and MAIA (Multi-Agent multicast routing based on Immune Algorithm).

The mathematical model of the turbine governing system is obtained by modeling analysis. Under the premise of this model, the improved genetic algorithm is used to optimize the parameters in the parameter identification of the turbine governing system, and the high-precision parameter identification of the turbine governing system is realized, and an accurate mathematical model of the turbine governing system is established. In terms of control strategy, through theoretical analysis of conventional PID, fuzzy control and genetic algorithm, a fuzzy PID control based on genetic algorithm optimization (GA-fuzzy PID) is designed to realize the adaptive and adjustment ability of PID control. Compared with the traditional PID control, it has better control effect, and it is easier to make the turbine adjustment system achieve stability, quickness and accuracy.

This paper presents a method of using the genetic algorithm (GA) with partial fitness (PF) to design a neural network for coin recognition. The method divides a chromosome in the GA into several parts, the PFs of which are evaluated for GA operations. Each part independently performs selection and crossover operations in the GA. Such a technique improves performance in learning of the GA. This paper applies the method to a rotated coin recognition problem to examine its effectiveness. The coin recognition system described consists of a preprocessor with Fourier transform and a multilayered network. The method is utilized to reduce the number of input signals, Fourier spectra, of the multilayered network. It is shown that the method is better than the conventional GA on convergence in learning and makes a smaller size network.

A new method is developed for finite element (FE) domain decomposition. This method employs a hybrid graph-genetic algorithm for graph partitioning and correspondingly bisects finite element (FE) meshes.A weighted incidence graph is first constructed for the FE mesh, denoted by G0. A coarsening process is then performed using heavy-edge matching. A sequence of such operations is employed in n steps, which leads to the formation of Gn with a size suitable for genetic algorithm applications.Hereafter, Gn is bisected using conventional genetic algorithm. The shortest route tree algorithm is used for the formation of the initial population in genetic algorithm. Then an uncoarsening process is performed and the results are transferred to the graph Gn-1. The initial population for genetic algorithm on Gn-1 is constructed from the results of Gn. This process is repeated until G0 is obtained in the uncoarsening operation. Employing the properties of G1, the graph G0 is bisected by the genetic algorithm.

Genetic algorithm (GA) is a common approach for multi-objective path planning. However, conventional GA performs poorly on large-scale complex maps due to the lack of an efficient initialization method and the infeasible solutions generated during the GA search. In this paper, first, we propose an innovative initialization method. The proposed method extracts the division points of the map and constructs a topological map, allowing the initialization of feasible paths based on the fitness function. Second, we calculate the estimated fitness value of each path in the topological map. Paths with low estimated fitness values will not be initialized, reducing the search space. In addition, we improve the crossover of GA, preventing the generation of infeasible paths by utilizing the topological map. The proposed method is compared against Theta*, A *(adjusted to consider smoothness), and conventional genetic algorithms on small and large-scale maps. The proposed method has outperformed previous methods regarding fitness value, reducing the runtime by more than 37% on large-scale maps.