Genetic programming (GP) automatically designs programs. evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, ...
详细信息
Genetic programming (GP) automatically designs programs. evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Levy distribution. This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics, exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that outperform automatically designed non-adaptive mutation operators.
evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro...
详细信息
evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro-mutation (MM), local linear bisection search (LBS) and crossover operators for global optimization. The MM operator is designed to explore the whole search space and the LBS operator to exploit the neighborhood of the solution. Simulated annealing is adopted to prevent premature convergence. The performance of the proposed algorithm is assessed by numerical experiments on 12 benchmark problems. Combined with MM, the effectiveness of various local search operators is also studied. (c) 2004 Elsevier B.V. All rights reserved.
Most of navigation techniques with obstacle avoidance do not consider the robot orientation at the target position. These techniques deal with the robot position only and are independent of its orientation and velocit...
详细信息
Most of navigation techniques with obstacle avoidance do not consider the robot orientation at the target position. These techniques deal with the robot position only and are independent of its orientation and velocity. To solve these problems this paper proposes a novel univector field method for fast mobile robot navigation which introduces a normalized two-dimensional vector field. The method provides fast moving robots with the desired posture at the target position and obstacle avoidance. To obtain the sub-optimal,vector field, a function approximator is used and trained by evolutionary programming. Tno kinds of vector fields are trained, one for the final posture acquisition and the other for obstacle avoidance. Computer simulations and real experiments are carried out for a fast moving mobile robot to demonstrate the effectiveness of the proposed scheme.
evolutionary programming and genetic algorithms are compared on two constrained optimization problems. The constrained problems are redesigned as related unconstrained problems by the application of penalty functions....
详细信息
evolutionary programming and genetic algorithms are compared on two constrained optimization problems. The constrained problems are redesigned as related unconstrained problems by the application of penalty functions. The experiments indicate that evolutionary programming outperforms the genetic algorithm. The results are statistically significant under nonparametric hypothesis testing. The results also indicate potential difficulties in the design of suitable penalty functions for constrained optimization problems. A discussion is offered regarding the suitability of different methods of evolutionary computation for such problems.
Of all the challenges faced by wireless sensor networks (WSN), extending the lifetime of the network has received the most attention from researchers. This issue is critically important, especially when sensors are de...
详细信息
Of all the challenges faced by wireless sensor networks (WSN), extending the lifetime of the network has received the most attention from researchers. This issue is critically important, especially when sensors are deployed to areas where it is practically impossible to charge their batteries, which are their only sources of power. Besides the development and deployment of ultra low-power devices, one effective computational approach is to partition the collection of sensors into several disjoint covers, so that each cover includes all targets, and then, activate the sensors of each cover one at a time.. This maximizes the possible disjoint covers with an available number of sensors and can be treated as a set-K cover problem, which has been proven to be NP-complete. evolutionary programming is a very powerful algorithm that uses mutation as the primary operator for evolution. Hence, mutation defines the quality and time consumed in the final solution computation. We have applied the self adaptive mutation strategy based on hybridization of Gaussian and Cauchy distributions to develop to develop a faster and better solution. One of the limitations associated with the evolutionary process is that it requires definition of the redundancy covers, and therefore, it is difficult to obtain the upper bound of a cover. To solve this problem, a redundancy removal operator that forces the evolution process to find a solution without redundancy is introduced. Through simulations, it is shown that the proposed method maximizes the lifespan of WSNs.
The disadvantages of conventional seismic tomographic ray tracing and inversion by calculus-based techniques include the assumption of a single ray path for each source-receiver pair, the non-inclusion of head waves, ...
详细信息
The disadvantages of conventional seismic tomographic ray tracing and inversion by calculus-based techniques include the assumption of a single ray path for each source-receiver pair, the non-inclusion of head waves, long computation times, and the difficulty in finding ray paths in a complicated velocity distribution. ii ray-tracing algorithm is therefore developed using the reciprocity principle and dynamic programming approach. This robust forward calculation routine is subsequently used for the cross-hole seismic velocity inversion. Seismic transmission tomography can be considered to be a function approximation problem;that is, of mapping the traveltime vector to the velocity vector. This falls under the purview of pattern classification problems, so we propose a forward-only counter-propagation neural network (CPNN technique for the tomographic imaging of the subsurface. The limitation of neural networks, however, lies in the requirement of exhaustive training for its use in routine interpretation. Since finding the optimal solution, sometimes from poor initial models, is the ultimate goal, global optimization and search techniques such as simulated evolution are also implemented in the cross-well traveltime tomography. Genetic algorithms (GA), evolution strategies and evolutionary programming (EP) are the main avenues of research in simulated evolution. Part of this investigation therefore deals with GA and EP schemes for tomographic applications. In the present work on simulated evolution, a new genetic operator called 'region-growing mutation' is introduced to speed up the search process. The potential of the forward-only CPNN, GA and EP methods is demonstrated in three synthetic examples. Velocity tomograms of the first model present plausible images of a diagonally orientated velocity contrast bounding two constant-velocity areas by both the CPNN and GA schemes, but the EP scheme could not image the model completely. In the second case, while GA and EP sc
evolutionary programming is mainly characterized by two factors: the selection strategy and the mutation rule. This letter presents a new mutation rule that has the same form as the well-known backpropagation learning...
详细信息
evolutionary programming is mainly characterized by two factors: the selection strategy and the mutation rule. This letter presents a new mutation rule that has the same form as the well-known backpropagation learning rule for neural networks. The proposed mutation rule assigns the best individual's fitness as the temporary target at each generation. The temporal error, the distance between the target and an individual at hand, is used to improve the exploration of the search space by guiding the direction of evolution. The momentum, i,e,, the accumulated evolution information for the individual, speeds up convergence. The efficiency and robustness of the proposed algorithm are assessed on several benchmark test functions.
Artificial intelligence techniques can be used to provide a second opinion in medical settings. This may improve the sensitivity and specificity of diagnoses, as well as the cost effectiveness of the physician's e...
详细信息
Artificial intelligence techniques can be used to provide a second opinion in medical settings. This may improve the sensitivity and specificity of diagnoses, as well as the cost effectiveness of the physician's effort. In the current study, evolutionary programming is used to train artificial neural networks to detect breast cancer using radiographic features and patient age. Results from 112 suspicious breast masses (63 malignant, 49 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved using simple neural architectures at the risk of a small percentage of false positives. (C) 1997 Elsevier Science Ireland Ltd.
A new generalized evolutionary programming with Levy-type mutation is proposed. The Levy-type distribution is know to reproduce Gaussian, Cauchy, and Student's t-distributions and characterized by a power-law fat-...
详细信息
A new generalized evolutionary programming with Levy-type mutation is proposed. The Levy-type distribution is know to reproduce Gaussian, Cauchy, and Student's t-distributions and characterized by a power-law fat-tail. This new evolutionary programming is tested for five standard test functions. The average performance of the new algorithm with Levy-type mutation for hard optimization problems is superior to the original evolutionary programming with Gaussian mutation. (C) 2002 Elsevier Science B.V. All rights reserved.
evolutionary programming (EP) has been successfully applied to many parameter optimization problems. We propose a mean mutation operator, consisting of a linear combination of Gaussian and Cauchy mutations. Preliminar...
详细信息
ISBN:
(纸本)0819425877
evolutionary programming (EP) has been successfully applied to many parameter optimization problems. We propose a mean mutation operator, consisting of a linear combination of Gaussian and Cauchy mutations. Preliminary results indicate that both the adaptive and non-adaptive versions of the mean mutation operator are capable of producing solutions that are as good as, or better than those produced by Gaussian mutations alone. The success of the adaptive operator could be attributed to its ability to self-adapt the shape of the probability density function that generates the mutations during the run.
暂无评论