Genetic Algorithm Performance with Different Selection Strategies in Solving TSP

A genetic algorithm (GA) has several genetic operators that can be modified to improve the performance of particular implementations. These operators include parent selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection. This paper presents the comparison of GA performance in solving travelling salesman problem (TSP) using different parent selection strategy. Several TSP instances were tested and the results show that tournament selection strategy outperformed proportional roulette wheel and rank-based roulette wheel selections, achieving best solution quality with low computing times. Results also reveal that tournament and proportional roulette wheel can be superior to the rank-based roulette wheel selection for smaller problems only and become susceptible to premature convergence as problem size increases

A new selection operator for genetic algorithms that balances between premature convergence and population diversity

The research objective is to find a balance between premature convergence and population diversity with respect to genetic algorithms (GAs). We propose a new selection scheme, namely, split-based selection (SBS) for GAs that ensures a fine balance between two extremes, i.e. exploration and exploitation. The proposed selection operator is further compared with five commonly used existing selection operators. A rigorous simulation-based investigation is conducted to explore the statistical characteristics of the proposed procedure. Furthermore, performance evaluation of the proposed scheme with respect to competing methodologies is carried out by considering 14 diverse benchmarks from the library of the traveling salesman problem (TSPLIB). Based on t-test statistic and performance index (PI), this study demonstrates a superior performance of the proposed scheme while maintaining the desirable statistical characteristics

A Heuristic Evolutionary Method for the Complementary Cell Suppression Problem

Cell suppression is a common method for disclosure avoidance used to protect sensitive information in two-dimensional tables where row and column totals are published along with non-sensitive data. In tables with only positive cell values, cell suppression has been demonstrated to be non-deterministic NP-hard. Therefore, finding more efficient methods for producing low-cost solutions is an area of active research. Genetic algorithms (GA) have shown to be effective in finding good solutions to the cell suppression problem. However, these methods have the shortcoming that they tend to produce a large proportion of infeasible solutions. The primary goal of this research was to develop a GA that produced low-cost solutions with fewer infeasible solutions created at each generation than previous methods without introducing excessive CPU runtime costs. This research involved developing a GA that produces low-cost solutions with fewer infeasible solutions produced at each generation; and implementing selection and replacement operations that maintained genetic diversity during the evolution process. The GA\u27s performance was tested using tables containing 10,000 and 100,000 cells. The primary criterion for the evaluation of effectiveness of the GA was total cost of the complementary suppressions and the CPU runtime. Experimental results indicate that the GA-based method developed in this dissertation produced better quality solutions than those produced by extant heuristics. Because existing heuristics are very effective, this GA-based method was able to surpass them only modestly. Existing evolutionary methods have also been used to improve upon the quality of solutions produced by heuristics. Experimental results show that the GA-based method developed in this dissertation is computationally more efficient than GA-based methods proposed in the literature. This is attributed to the fact that the specialized genetic operators designed in this study produce fewer infeasible solutions. The results of these experiments suggest the need for continued research into non-probabilistic methods to seed the initial populations, selection and replacement strategies that factor in genetic diversity on the level of the circuits protecting sensitive cells; solution-preserving crossover and mutation operators; and the use of cost benefit ratios to determine program termination

Structural Basis of Caspase-3 Substrate Specificity Revealed by Crystallography, Enzyme Kinetics, and Computational Modeling

Caspase-3 is a cysteine protease that hydrolyzes diverse intracellular proteins during programmed cell death (known as apoptosis). It has been a popular target for drug design against abnormal cell death for more than a decade. No approved caspase based drug, however, is available so far. Therefore, structural insights about the substrate recognition of caspase-3 are needed for the future development of caspase-3 based inhibitors and drugs. In this study, crystal structures of recombinant caspase-3 in complex with seven substrate analog inhibitors, including acetyl (Ac)-DEVD-aldehyde (Cho), Ac-DMQD-Cho, Ac-IEPD-Cho, Ac-YVAD-Cho, Ac-WEHD-Cho, Ac-VDVAD-Cho, and tert-butoxycarbonyl (Boc)-D-fluoromethylketone (Fmk), have been analyzed in combination with enzyme kinetic data and computational models. Seven crystal structures were determined at resolutions of 1.7-2.6Å. The binding conformation of each inhibitor residue at P1-P4 position was analyzed. The negative P1 aspartic acid side chain is exclusively required by the positive S1 pocket of caspase-3. Small hydrophobic P2 residues are preferred by the nonpolar S2 pocket formed by Y204, W206, and F256. Although hydrophilic residues at P3 position tend to fit better, hydrophobic residues also can be accommodated by the plastic S3 pocket. Two substrate binding sites were found in the S4 pocket, one formed by main chain atoms of F250 and side chain atoms of N208 and the other formed by aromatic side chains of W206 and W214. These two binding sites are responsible for the binding of hydrophilic and hydrophobic P4 residues, respectively. Furthermore, the S5 subsite of caspase-3 formed by side chains of F250 and F252 was discovered. It stabilizes hydrophobic P5 residues on the substrates by an induced fit mechanism. Computational studies were performed to help improve prediction of protein structures and protein-ligand interactions. Based on the Morse’s function, a novel potential function with only three adjustable parameters per residue pair was developed, which will significantly increase the efficiency of protein structure prediction and molecular mechanics. Altogether, our studies have provided valuable information for the future caspase-3 based drug development