Fitness Clouds and Problem Hardness in Genetic Programming
نویسندگان
چکیده
This paper presents an investigation of genetic programming fitness landscapes. We propose a new indicator of problem hardness for tree-based genetic programming, called negative slope coefficient, based on the concept of fitness cloud. The negative slope coefficient is a predictive measure, i.e. it can be calculated without prior knowledge of the global optima. The fitness cloud is generated via a sampling of individuals obtained with the Metropolis-Hastings method. The reliability of the negative slope coefficient is tested on a set of well known and representative genetic programming benchmarks, comprising the binomial-3 problem, the even parity problem and the artificial ant on the Santa Fe trail.
منابع مشابه
Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes
Negative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents ...
متن کاملGenetic Programming with Historically Assessed Hardness
We present a variation of the genetic programming algorithm, called Historically Assessed Hardness (HAH), in which the fitness rewards for particular test cases are scaled in proportion to their relative difficulty as gauged by historical solution rates. The method is similar in many respects to some realizations of techniques such as implicit fitness sharing, stepwise adaptation of weights and...
متن کاملDimensionality Reduction and Improving the Performance of Automatic Modulation Classification using Genetic Programming (RESEARCH NOTE)
This paper shows how we can make advantage of using genetic programming in selection of suitable features for automatic modulation recognition. Automatic modulation recognition is one of the essential components of modern receivers. In this regard, selection of suitable features may significantly affect the performance of the process. Simulations were conducted with 5db and 10db SNRs. Test and ...
متن کاملAutomatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm
Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (...
متن کاملA Flexible Job Shop Scheduling Problem with Controllable Processing Times to Optimize Total Cost of Delay and Processing
In this paper, the flexible job shop scheduling problem with machine flexibility and controllable process times is studied. The main idea is that the processing times of operations may be controlled by consumptions of additional resources. The purpose of this paper to find the best trade-off between processing cost and delay cost in order to minimize the total costs. The proposed model, flexibl...
متن کامل