Kunihito Yamamori

Implementation of an Effective Hybrid GA for Large-Scale Traveling Salesman Problems

This correspondence describes a hybrid genetic algorithm (GA) to find high-quality solutions for the traveling salesman problem (TSP). The proposed method is based on a parallel implementation of a multipopulation steady-state GA involving local search heuristics. It uses a variant of the maximal preservative crossover and the double-bridge move mutation. An effective implementation of the Lin-Kernighan heuristic (LK) is incorporated into the method to compensate for the GAs lack of local search ability. The method is validated by comparing it with the LK-Helsgaun method (LKH), which is one of the most effective methods for the TSP. Experimental results with benchmarks having up to 316 228 cities show that the proposed method works more effectively and efficiently than LKH when solving large-scale problems. Finally, the method is used together with the implementation of the iterated LK to find a new best tour (as of June 2, 2003) for a 1 904 711-city TSP challenge

A parallel hybrid genetic algorithm for multiple protein sequence alignment

This paper presents a parallel hybrid genetic algorithm (GA) for solving sum-of-pairs multiple protein sequence alignment. The method is based on a multiple population GENITOR-type GA and involves local search heuristics. It is then extended to parallel to exploit the benefit of a multiprocessor system. Benchmarks from the BAliBASE library are used to validate the method.

Enhancement of the Variable-Length-Transmission-Line design method for multi-point optimization

Distortion of the waveform on printed circuit board (PCBs) is a serious problem in higher-frequency signals transmission. To overcome this problem, we have already proposed segmental transmission line (STL). The STL divides transmission lines into multiple segments with different line widths, those are adjusted to reshape the signal waveform by superposition of reflected waves occurring at segment boundaries. In the previous work, we reshaped the waveform at only one point in the transmission line connected with a device at a time. This paper expands the method to reshape the waveform at two points of the devices at a time. We design a DIMM (dual in-line memory module) clock-line for high-speed computers using our new method, and show effectiveness of the method.

Improved GA-based method for multiple protein sequence alignment

In previous work, we have proposed a parallel hybrid genetic algorithm (PHGA) which can find high quality solution from the mathematical viewpoint for the multiple protein sequence alignment. We present new improvements to the PHGA. Local alignment information is added to the weighted sum-of-pairs objective function to achieve better alignment from the biological viewpoint. We also extend our method to run in parallel on a cluster of machines instead of a multi-processor machine to speed it up.

Variable length segmental-transmission-line and its parameter optimization based on GA

Waveform distortion is a serious problem in higher-frequency signals on printed circuit boards (PCB). In order to overcome this problem, the segmental transmission line (STL) has already been proposed. However, the STL requires segments of wide-range widths. The wide-range line-width impedes higher optimization of waveforms and it also causes cost-increment in the PCB manufacturing. In this paper, we propose a novel STL with variable length segments in order to overcome the difficulties in previous work. We also develop a design methodology using a GA to determine the parameters for the proposed STL. We apply our new STL to the clock line in the actual PCB, and show its effectiveness compared with the previously proposed STL.

Efficient approach for simultaneous estimation of multiple health-promoting effects of foods.

The investigation of new food constituents for purposes of disease prevention or health promotion is an area of increasing interest in food science. This paper proposes a new system that allows for simultaneous estimation of the multiple health-promoting effects of food constituents using informatics. The model utilizes expression data of intracellular marker proteins as descriptors that reply to stimulation of a constituent. To estimate three health-promoting effects, namely, cancer cell growth suppression activity, antiviral activity, and antioxidant stress activity, each model was constructed using expression data of marker proteins as input data and health-promoting effects as the output value. When prediction performances of three types of mathematical models constructed by simple, multiple regressions, or artificial neural network (ANN), were compared, the most adequate model was the one constructed using an ANN. There were no statistically significant differences between the actual data and estimated values calculated by the ANN models. This system was able to simultaneously estimate health-promoting effects with reasonable precision from the same expression data of marker proteins. This novel system should prove to be an interesting platform for evaluation of the health-promoting effects of food.

A proposal for Zoning Crossover of Hybrid Genetic Algorithms for large-scale traveling salesman problems

This paper proposes a novel crossover operator for solving large-scale traveling salesman problems (TSPs) by using a Hybrid Genetic Algorithm (HGA) with Lin-Kernighan heuristic for local search and we tentatively name Zoning Crossover (Z-Cross). The outline of Z-Cross is firstly to set a zone in the travelling area according to some rules, secondly to cut edges connecting cities between inside and outside the zone, thirdly to exchange edges inside the zone of one parent and those of the other parent, and lastly to reconnect sub-tours and isolated cities, which come about in the 3rd step mentioned above, so as to construct a new tour of TSP. The method is compared with conventional three crossovers; those are the Maximal Preservative Crossover, the Greedy Sub-tour Crossover and the Edge Recombination Crossover, and evaluated from the viewpoints of tour quality and CPU time. Ten benchmarks are selected from the well-known TSP website of Georgia Institute of Technology, whose names are xqf131, xqg237, …, sra104815. The experiments are performed ten times for each crossover and each benchmark and show that the Z-Cross succeeds in finding better solution and running faster than the conventional methods. Six benchmarks with size from 39,603 to 104,815 cities are selected from the TSP website and challenged the records of tour lengths. The Z-Cross betters the record of the problem rbz43748 and approaches to solutions less than only 0.02% over the known best solutions for five instances.

A New Three-Level Tree Data Structure for Representing TSP Tours in the Lin-Kernighan Heuristic

Lin-Kernighan (LK) is the most powerful local search for the Traveling Salesman Problem (TSP). The choice of data structure for tour representation plays a vital role in LKs performance. Binary trees are asymptotically the best tour representation but they perform empirically best only for TSPs with one million or more cities due to a large overhead. Arrays and two-level trees are used for smaller TSPs. This paper proposes a new three-level tree data structure for tour representation. Although this structure is asymptotically not better than the binary tree structure, it performs empirically better than the conventional structures for TSPs having from a thousand to three million cities.

Asynchronous migration for parallel genetic programming on a computer cluster with multi-core processors

An island model is a typical implementation of genetic programming on parallel computers with distributed memory. The island model has a migration facility that sends/receives some individuals in an island to/from another island to maintain diversity. The island model requires synchronization to migrate same-generation individuals between islands, and this synchronization causes an increase in computation time. This article proposes a new parallel genetic programming implementation based on the island model with asynchronous migration. Most recent computers are equipped with one or more multi-core processors, and are suitable for multi-threading. Therefore we employ a communication thread for migration between islands. The communication thread on a processor communicates with the communication thread on another processor to migrate individuals at appropriate intervals. Since the migration and other genetic operations can be independently processed on each core, and since we allow the exchange of individuals of different generations, no synchronization is needed in our implementation. In addition, a fitness calculation is also executed in parallel by the remaining cores. Experimental results show that the proposed method can reduce the computation time to about 17% in serial GP by using 40 threads.

Development of a novel crossover of hybrid genetic algorithms for large-scale traveling salesman problems

This article proposes a novel crossover operator of hybrid genetic algorithms (HGAs) with a Lin-Kernighan (LK) heuristic for solving large-scale traveling salesman problems (TSPs). The proposed crossover, tentatively named sub-tour recombination crossover (SRX), collects many short sub-tours from both parents under some set of rules, and reconnects them to construct a new tour of the TSP. The method is evaluated from the viewpoint of tour quality and CPU time for ten well-known benchmarks, e.g., dj38, qa194, …, ch71009.tsp, in the TSP website of the Georgia Institute of Technology. We compare the SRX with three conventional crossover operators, a variant of the maximal preservative crossover operator (MPX3), a variant of the greedy sub-tour crossover operator (GSX2), and a variant of the edge recombination crossover operator (ERX6), and show that the SRX succeeded in finding a better solution and running faster than the conventional methods mentioned above.