Pseudo-bacterial Genetic Algorithm Research Articles

Evolutionary correlation filtering based on pseudo-bacterial genetic algorithm for pose estimation of highly occluded targets

An accurate method based on evolutionary correlation filtering to solve pose estimation of highly occluded targets is presented. The proposed method performs multiple correlation operations between an input scene and a bank of filters designed in frequency-domain. Each filter is computed with statistical parameters of a real-world scene and a template that contains information of the target in a single pose parameter configuration. A vast set of templates is generated from multiple views of a three-dimensional model of the target, which are created synthetically with computer graphics. An evolutionary approach in the bank of filter construction for optimizing the pose estimation parameters is implemented. The evolutionary computation technique based on a pseudo-bacterial genetic algorithm yields high estimation accuracy finding the best filter that produces the highest matching score. The proposed evolutionary correlation filtering yields good convergence of the bank of filter optimization, which produces a reduction of the number of computational operations. Experimental results demonstrate the robustness of the proposed method in terms of detection performance and pose estimation of highly occluded targets compared with state-of-the-art methods.

Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios.

Pseudo-Bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation

This paper introduces the pseudo-bacterial potential field (PBPF) as a new path planning method for autonomous mobile robot navigation. The PBPF allows us to obtain an optimal and safe path, in contrast to the classical potential field approach which is not suitable for path planning because it lacks a means of obtaining the optimal proportional gains. The PBPF uses the pseudo-bacterial genetic algorithm (PBGA) and a fitness function based on the potential field concepts to construct viable paths in dynamical environments to mostly result in the optimal path being obtained. Comparative experiments of sequential and parallel implementations of the PBPF for off-line and online in structured and unstructured conditions are presented; the results are contrasted with the artificial potential field (APF) method to demonstrate how the PBPF proposal overcomes the traditional method.

A high performance genetic algorithm using bacterial conjugation operator (HPGA)

In this paper an efficient evolutionary algorithm is proposed which could be applied to real-time problems such as robotics applications. The only parameter of the proposed algorithm is the “Population Size” which makes the proposed algorithm similar to parameter-less algorithms, and the only operator applied during the algorithm execution is the bacterial conjugation operator, which makes using and implementation of the proposed algorithm much easier. The procedure of the bacterial conjugation operator used in this algorithm is different from operators of the same name previously used in other evolutionary algorithms such as the pseudo bacterial genetic algorithm or the microbial genetic algorithm. For a collection of 23 benchmark functions and some other well-known optimization problems, the experimental results show that the proposed algorithm has better performance when compared to particle swarm optimization and a simple genetic algorithm.

Cerebellar model arithmetic computer with pseudo‐bacterial genetic algorithm and its hardware acceleration

AbstractThe cerebellar model arithmetic computer (CMAC) is a kind of neural network that is capable of high‐speed learning. In the use of the CMAC, various parameters must be defined, but it is difficult to set the parameters adequately. The pseudo‐bacterial genetic algorithm (PBGA) is a kind of evolutionary computation which offers local search. This paper considers adequate determination of the location of the boundary line, which is one of the parameters of the CMAC. For that purpose the PBGA is applied, and PBGA/CMAC, which realizes the efficient search of the boundary line of the CMAC, is proposed. Furthermore, since PBGA/CMAC requires a large amount of computation, a hardware implementation is considered in order to improve the computation speed. The computation involved in the learning rules of the CMAC is divided into four stages, and the structure is implemented as a pipeline. The pipeline implementation produces read‐after‐write hazards for the granular cell values. However, it is shown that the difference in performance from conventional CMAC learning rule is small, indicating the possibility of achieving high‐speed learning. The speed of PBGA/CMAC hardware is improved to 140 times the speed of operation by software. In order to present a guide for PBGA/CMAC hardware, the hardware volume is evaluated, and it is shown that it can be implemented with precision sufficient for practice. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(11): 14–23, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10437

A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm

This paper presents a new method for the discovery of relevant fuzzy rules using the pseudobacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm with a local improvement mechanism inspired by a process in bacterial genetics, named bacterial operation. The presented system aims at the improvement of the quality of the generated fuzzy rules, producing blocks of effective rules and more compact rule bases. This is achieved by encoding the fuzzy rules in the chromosomes in a suitable form in order to make the bacterial operation more effective and by using a crossover operation that adaptively decides the cutting points according to the distribution of degrees of truth values of the rules. In this paper, first, results obtained when using the PBGA for a simple fuzzy modeling problem are presented and compared with other methods. Second, the PBGA is used in the design of a fuzzy logic controller for a semi-active suspension system. The results show the benefits obtained with this approach in both of the studied cases.

Fuzzy system parameters discovery by bacterial evolutionary algorithm

This paper presents a new method for discovering the parameters of a fuzzy system; namely, the combination of input variables of the rules, the parameters of the membership functions of the variables, and a set of relevant rules from numerical data using the newly proposed bacterial evolutionary algorithm (BEA). Nawa et al. (1997) proposed the pseudobacterial genetic algorithm (PBGA) that incorporates a modified mutation operator called bacterial mutation, based on a biological phenomenon of microbial evolution. The BEA has the same features of the PBGA, but introduces a new operation, called gene transfer operation, equally inspired by a microbial evolution phenomenon. While the bacterial mutation performs local optimization within the limits of a single chromosome, the gene transfer operation allows the chromosomes to directly transfer information to the other counterparts in the population. The gene transfer is inspired by the phenomenon of transfer of strands of genes in a population of bacteria. By means of this mechanism, one bacterium can rapidly spread its genetic information to other cells. Numerical experiments were performed to show the effectiveness of the BEA. The obtained results show the benefits that can be obtained with this method.