In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function’s parameters for
computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with
top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved
by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program
consists of a much smaller number of parameters than the expert’s. The extended experimental results provided in this paper
include a report on our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven
approach could be used in a wide range of problems for which appropriate experts are available. 相似文献
This paper aims to understand and optimize the crush response of Functionally Graded Thickness (FGT) tubes with various thickness distributions subjected to oblique loading using multi-objective optimization method. Hence, finite element (FE) models are established and their results are validated by experimental tests. Two objective functions (specific energy absorption and peak load) are approximated by four different multi-objective optimization models: the weighted average, multi-design optimization (MDO) technique, constrained single-objective optimization, and geometrical average methods. The optimum design results demonstrate that the selection of appropriate inversion tube parameters such as the die radius, the coefficient of friction between the die and tube, and thickness distribution function have significant roles in the crashworthiness design. The results give new ideas to improve the crashworthiness performance of inversion tubes under oblique loading conditions. 相似文献
Optimization algorithms are important tools for the solution of combinatorial management problems. Nowadays, many of those problems are addressed by using evolutionary algorithms (EAs) that move toward a near-optimal solution by repetitive simulations. Sometimes, such extensive simulations are not possible or are costly and time-consuming. Thus, in this study a method based on artificial neural networks (ANN) is proposed to reduce the number of simulations required in EAs. Specifically, an ANN simulator is used to reduce the number of simulations by the main simulator. The ANN is trained and updated only for required areas in the decision space. Performance of the proposed method is examined by integrating it with the non-dominated sorting genetic algorithm (NSGAII) in multi-objective problems. In terms of density and optimality of the Pareto front, the hybrid NSGAII-ANN is able to extract the Pareto front with much less simulation time compared to the sole use of the NSGAII algorithm. The proposed NSGAII-ANN methodology was examined using three standard test problems (FON, KUR, and ZDT1) and one real-world problem. The latter addresses the operation of a reservoir with two objectives (meeting demand and flood control). Thus, based on this study, use of the NSGAII-ANN integrative algorithm in problems with time-consuming simulators reduces the required time for optimization up to 50 times. Results of the real-world problem, despite lower computational-time requirements, show a performance similar to that achieved in the aforementioned test problems. 相似文献
This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.
Nanotechnology has the potential to revolutionize cancer diagnosis and therapy. Advances in protein engineering and materials science have contributed to novel nanoscale targeting approaches that may bring new hope to cancer patients. Several therapeutic nanocarriers have been approved for clinical use. However, to date, there are only a few clinically approved nanocarriers that incorporate molecules to selectively bind and target cancer cells. This review examines some of the approved formulations and discusses the challenges in translating basic research to the clinic. We detail the arsenal of nanocarriers and molecules available for selective tumour targeting, and emphasize the challenges in cancer treatment. 相似文献
Group decision making with multiple criteria is the most popular method for ranking a set of alternatives. In this regard, all alternatives are compared based on a common criteria set. Meanwhile, decision makers sometimes encounter special situations, for example, having to select from among a set of alternatives without a set of criteria or with a set of criteria that are grouped/related to various alternatives. Thus, it may be impossible to select from among a set of alternatives using typical methods. Hence, in this study, a new, modified VIKOR method is proposed to address the lean tool selection problems in manufacturing systems. In this study, a model was developed to help practitioners improve their ability to solve problems when the possible solutions have their own individual criteria. In fact the modified VIKOR method can be applied to rank alternatives in threefold: alternatives with common criteria, without common criteria, with integrated common, and uncommon criteria. This paper offers numerical examples of the model, using a case study to illustrate an application of the proposed model and properly assess the validity of this new method. The results demonstrate the usefulness and effectiveness of the modified new method. The model covers the lack existing in the current literature to assess effectiveness of applying lean tools. 相似文献
In this study, NiTi–x wt.% B4C (x = 0, 2, and 4) composites were consolidated with spark plasma sintering method, and the effects of boron carbide reinforcement addition on the microstructure and wear behavior of samples were investigated. Identification of the constituent phases of samples by the X-ray diffraction method plus Rietveld analysis revealed that the stability of the martensite phase increased in the composite samples because of mismatch stresses between the NiTi matrix phase and the reinforcing particles, which increases the density of the dislocations and facilitates the diffusion process that subsequently leads to the formation of stable intermetallics. The results of hardness test indicated that the hardness value increased from 3.67 GPa for pure NiTi to 10.99 GPa for NiTi–4 wt.% B4C. Results of wear test revealed that boron carbide reinforced composite specimens had higher wear resistance, whereas wear rate of NiTi sample was 3.6 × 10−3 mm3/N m, and it reached to .21 × 10−3 mm3/N m for NiTi–4 wt.% B4C. Investigation of microstructure by scanning electron microscopy images and EDS analysis revealed that the wear mechanism in NiTi samples was abrasive and the addition of B4C to NiTi changed the wear mechanisms from abrasive to a combination of oxidation, adhesive, and delamination mechanisms. 相似文献