Four experiments investigated transformation problems with insight characteristics. In Experiment 1, performance on a version of the 6-coin problem that had a concrete and visualizable solution followed a hill-climbing heuristic. Experiment 2 demonstrated that the difficulty of a version of the problem that potentially required insight for solution stems from the same hill-climbing heuristic, w...

In this paper we address the problem of program discovery as deened by Genetic Programming 10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer tness evaluations and a greater probability of a success than Geneti...

The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginnin...

There is a variety of knapsack problems in the literature. Multidimensional 0-1 Knapsack Problem (MKP) is an NP-hard combinatorial optimization problem having many application areas. Many approaches have been proposed for solving this problem. In this paper, an empirical investigation of memetic algorithms (MAs) that hybridize genetic algorithms (GAs) with hill climbing for solving MKPs is prov...

C4.5 algorithm is the most widely used algorithm in the decision trees so far and obviously the most popular heuristic function is gain ratio. This heuristic function has a serious disadvantage – towards dealing with irrelevant featured data sources. The hill climbing is a machine learning technique used in searching. It has good searching mechanism. Considering the relationship between hill cl...

Consider the problem of control selection in complex dynamical and environmental scenarios where model predictive control (MPC) proves particularly effective. As the performance of MPC is highly dependent on the efficiency of its incorporated search algorithm, this work examined hill climbing as an alternative to traditional systematic or random search algorithms. The relative performance of a ...

Genetic Algorithms are biologically inspired optimization algorithms. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Crossover operators are used to bring diversity in the population. This paper studies different crossover operators and then proposes a hybrid crossover operator that incorporates knowledg...

The main weakness of shogi programs is considered to be in the opening and the middle game. Deep search is not enough to cover the lack of strategic understanding, so most strong shogi programs use a hill-climbing approach to build castle and assault formations. The problem of a hill-climbing approach is that if the final result of two different paths is the same, then the final score will also...

Biometric recognition systems are vulnerable to numerous security threats. These include direct attacks to the sensor or indirect attacks, which represent the ones aimed towards internal system modules. In this work, indirect attacks against fingerprint verification systems are analyzed in order to better understand how harmful they can be. Software attacks via hill climbing algorithms are impl...

These last years two global optimizations methods hybridizing Evolutionary Algorithms (EA, but mainly GA) with hill-climbing methods have been investigated. The first one involves two interwoven levels of optimization: Evolution (EA) and Individual Learning (hill-climbing), which cooperate in the global optimization process. The second one consists of modifying EA by the introduction of new gen...