Chuan-Kang Ting

An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications

Wireless sensor network lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. One approach to extend the lifetime is to divide the deployed sensors into disjoint subsets of sensors, or sensor covers, such that each sensor cover can cover all targets and work by turns. The more sensor covers can be found, the longer sensor network lifetime can be prolonged. Finding the maximum number of sensor covers can be solved via transformation to the Disjoint Set Covers (DSC) problem, which has been proved to be NP-complete. For this optimization problem, existing heuristic algorithms either get unsatisfactory solutions in some cases or take exponential time complexity. This paper proposes a genetic algorithm to solve the DSC problem. The simulation results show that the proposed algorithm can get near-optimal solutions with polynomial computation time and can improve the performance of the most constrained-minimum constraining heuristic algorithm by 16% in solution quality.

A memetic algorithm for extending wireless sensor network lifetime

Extending the lifetime during which a wireless sensor network (WSN) can cover all targets is a key issue in WSN applications such as surveillance. One effective method is to partition the collection of sensors into several covers, each of which must include all targets, and then to activate these covers one by one. Therefore, more covers enable longer lifetime. The problem of finding the maximum number of covers has been modeled as the SET K-COVER problem, which has been proven to be NP-complete. This study proposes a memetic algorithm to solve this problem. The memetic algorithm utilizes the Darwinian evolutionary scheme and Lamarckian local enhancement to search for optima given the considerations of global exploration and local exploitation. Additionally, the proposed algorithm does not require an upper bound or any assumption about the maximum number of covers. The simulation results on numerous problem instances confirm that the algorithm significantly outperforms several heuristic and evolutionary algorithms in terms of solution quality, which demonstrate the effectiveness of the proposed algorithm in extending WSN lifetime.

Wireless Heterogeneous Transmitter Placement Using Multiobjective Variable-Length Genetic Algorithm

The problem of placing wireless transmitters to meet particular objectives, such as coverage and cost, has proven to be NP-hard. Furthermore, the heterogeneity of wireless networks makes the problem more intractable to deal with. This paper presents a novel multiobjective variable-length genetic algorithm to solve this problem. One does not need to determine the number of transmitters beforehand; the proposed algorithm simultaneously searches for the optimal number, types, and positions of heterogeneous transmitters by considering coverage, cost, capacity, and overlap. The proposed algorithm can achieve the optimal number of transmitters with coverage exceeding 98% on average for six benchmarks. These preferable experimental results demonstrate the high capability of the proposed algorithm for the wireless heterogeneous transmitter placement problem.

On the mean convergence time of multi-parent genetic algorithms without selection

This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact model based on Markov chains is proposed to formulate the variation of gene frequency. This model identifies the correlation between the adopted number of parents and the mean convergence time. Moreover, it reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs. The good fit between theoretical and experimental results further verifies the capability of this model.

On the harmonious mating strategy through tabu search

Genetic algorithms (GAs) are well-known heuristic algorithms and have been applied to solve a variety of complicated problems. When adopting GA approaches, two important issues--selection pressure and population diversity--must be considered. This work presents a novel mating strategy, called tabu genetic algorithm (TGA), which harmonizes these two issues by integrating tabu search (TS) into GAs selection. TGA incorporates the tabu list to prevent inbreeding so that population diversity can be maintained, and further utilizes the aspiration criterion to supply moderate selection pressure. An accompanied self-adaptive mutation method is also proposed to overcome the difficulty of determining mutation rate, which is sensitive to computing performance. The classic traveling salesman problem is used as a benchmark to validate the effectiveness of the proposed algorithm. Experimental results indicate that TGA can achieve harmony between population diversity and selection pressure. Comparisons with GA, TS, and hybrids of GA and TS further confirm the superiority of TGA in terms of both solution quality and convergence speed.

Multi-parent extension of partially mapped crossover for combinatorial optimization problems

This paper proposes the multi-parent partially mapped crossover (MPPMX), which generalizes the partially mapped crossover (PMX) to a multi-parent crossover. The mapping list and legalization of PMX are modified to deal with the issues that arise from the increase of parents in PMX. Experimental results on five traveling salesman problems show that MPPMX significantly improves PMX by up to 13.95% in mean tour length. These preferable results not only demonstrate the advantage of the proposed MPPMX over PMX, but also confirm the merit of using more than two parents in crossover.

An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction

BackgroundProteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, DNA and RNA synthesis, and embryonic development. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein from its primary amino acid sequence. From visual comparison, it was found that a 2D triangular lattice model can give a better structure modeling and prediction for proteins with short primary amino acid sequences.MethodsThis paper proposes a hybrid of hill-climbing and genetic algorithm (HHGA) based on elite-based reproduction strategy for protein structure prediction on the 2D triangular lattice.ResultsThe simulation results show that the proposed HHGA can successfully deal with the protein structure prediction problems. Specifically, HHGA significantly outperforms conventional genetic algorithms and is comparable to the state-of-the-art method in terms of free energy.ConclusionsThanks to the enhancement of local search on the global search, the proposed HHGA achieves promising results on the 2D triangular protein structure prediction problem. The satisfactory simulation results demonstrate the effectiveness of the proposed HHGA and the utility of the 2D triangular lattice model for protein structure prediction.

Multi-objective tag SNPs selection using evolutionary algorithms

MOTIVATION Integrated analysis of single nucleotide polymorphisms (SNPs) and structure variations showed that the extent of linkage disequilibrium is common across different types of genetic variants. A subset of SNPs (called tag SNPs) is sufficient for capturing alleles of bi-allelic and even multi-allelic variants. However, accuracy and power of tag SNPs are affected by several factors, including genotyping failure, errors and tagging bias of certain alleles. In addition, different sets of tag SNPs should be selected for fulfilling requirements of various genotyping platforms and projects. RESULTS This study formulates the problem of selecting tag SNPs into a four-objective optimization problem that minimizes the total amount of tag SNPs, maximizes tolerance for missing data, enlarges and balances detection power of each allele class. To resolve this problem, we propose evolutionary algorithms incorporated with greedy initialization to find non-dominated solutions considering all objectives simultaneously. This method provides users with great flexibility to extract different sets of tag SNPs for different platforms and scenarios (e.g. up to 100 tags and 10% missing rate). Compared to conventional methods, our method explores larger search space and requires shorter convergence time. Experimental results revealed strong and weak conflicts among these objectives. In particular, a small number of additional tag SNPs can provide sufficient tolerance and balanced power given the low missing and error rates of todays genotyping platforms. AVAILABILITY The software is freely available at Bioinformatics online and http://cilab.cs.ccu.edu.tw/service_dl.html.

Varying Number of Difference Vectors in Differential Evolution

Differential evolution (DE) has shown its effectiveness in solving many problems. The difference vector (DV), which serves as a measure for the dispersion of candidate solutions, has a key role in the adaptive mutation of DE. Traditionally, DE adopts one DV. In this paper, we investigate the use of more than one DV and propose the Poisson differential evolution (PDE) with a varying number of DVs based on Poisson distribution. Experimental results on 24 numerical benchmark functions point out the ineffectiveness of increasing DVs in the original DE. On the other hand, the results show that the proposed PDE can achieve significant improvement on DE in terms of solution quality and convergence speed, which validates the benefit of varying number of DVs for DE.

Evolutionary composition using music theory and charts

With the development of human science and technology, applications of computer are more and more comprehensive. Using artificial intelligence (AI) to drawing, thinking, and problem solving becomes a significant topic. Recently, research on automatic composition using AI technology and especially evolutionary algorithms is blooming and has received promising results. A common issue at the current evolutionary composition systems is their requirement for subjective feedback of human sensation as evaluation criterion, which is vulnerable to the fatigue and decreased sensitivity after long-time listening. This paper proposes using music theory with the information from music charts in the evaluation criterion to address this issue. Specifically, we generate the weighted rules based on music theory for the fitness function. The weights are determined according to the download numbers from music charts. These weights obtained can interpret the music style and render an objective measure of compositions. Experimental results show that the proposed method can effectively achieve satisfactory compositions.