Searching in the presence of noise

Rana, Soraya B.; Whitley, L. Darrell; Cogswell, Ronald

doi:10.1007/3-540-61723-x_984

Cited by 36 publications

(28 citation statements)

References 5 publications

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“…Earlier, the effects of additive Gaussian noise have been demonstrated on a set of five test functions (Rana et al, 1996). The conclusions of the study were that after adding noise, some cases exhibited soft annealing effect while most …”

Section: Experimental Design For Parameter Tuningmentioning

confidence: 92%

“…Many works have been reported in literature in order to map the system chaos and noise within the working dimensions of EAs (Nissen and Propach, 1998;Rana et al, 1996;Caponetto et al, 2003). Caponetto et al (2003) introduced chaotic sequences instead of random sequences during all the phases of random evolution process.…”

Section: Binary Stringmentioning

confidence: 99%

“…In the present scenario, evolutionary algorithms (EAs) are not limited to the applications in artificial intelligence, and have been increasingly used in various dimensions to solve real world problems (Dimopoulos and Zalzala, 2000;Sinha et al, 2003). Although, a plethora of literature pertaining to the search strategies adopted by various EAs is available (Nissen and Propach, 1998;Rana et al, 1996;Kazarlis et al, 2001;Yao et al, 1999;Salomon, 1998;Choi and Oh, 2000;Yoon and Moon, 2002;Kim and Myung, 1997;Storn, 1999), most of them restrict themselves to solving a particular class of optimization problem and thus, fail to provide generalized strategies that can be robustly used for wide spectrum of optimization problems in science, business and engineering applications. In general, optimization problems can be classified into two groupsnumerical optimization and combinatorial optimization (Tsai et al, 2004;Gen and Cheng, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…In general, optimization problems can be classified into two groupsnumerical optimization and combinatorial optimization (Tsai et al, 2004;Gen and Cheng, 1999). Yet another classification exists that is based on the representation schemes-binary string, floating point string and integer bit string representation (Rana et al, 1996;Kazarlis et al, 2001;Yao et al, 1999;Salomon, 1998;Choi and Oh, 2000; Yoon and Moon, 2002;Kim and Myung, 1997;Storn, 1999;Zhang and Leung, 1999;Burke and Newall, 1999). In general, the first two representations are more suited and easy to implement on numerical optimization problems; whereas, the third representation enjoys solving the combinatorial optimization problems with a greater ease and efficiency (Nijssen and Ba¨ck, 2003;Goldberg, 1989).…”

Section: Introductionmentioning

confidence: 99%

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Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Dashora

Kumar

Shukla³

et al. 2008

Engineering Applications of Artificial Intelligence

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This paper characterizes general optimization problems into four categories based on the solution representation schemes, as they have been the key to the design of various evolutionary algorithms (EAs). Four EAs have been designed for different formulations with the aim of utilizing similar and generalized strategies for all of them. Several modifications to the existing EAs have been proposed and studied. First, a new tradeoff function-based mutation has been proposed that takes advantages of Cauchy, Gaussian, random as well as chaotic mutations. In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search. A theoretical analysis has been performed to establish the convergence of the learning rule. A theoretical study has also been performed in order to investigate the various aspects of the search strategy employed by the new tradeoff-based mutations. A more logical parameter tuning has been done by introducing the concept of orthogonal arrays in the EA experimentation. The use of noise-based tuning ensures the robust parameter tuning that enables the EAs to perform remarkably well in the further experimentations. The performance of the proposed EAs has been analyzed for different problems of varying complexities. The results prove the supremacy of the proposed EAs over other well-established strategies given in the literature. r

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Section: Experimental Design For Parameter Tuningmentioning

confidence: 92%

Section: Binary Stringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Dashora

Kumar

Shukla³

et al. 2008

Engineering Applications of Artificial Intelligence

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“…CHC has been found to outperform GGA's (100,101,103) and GENITOR (93) . In addition, CHC has been found to require less parameter tuning than other genetic algorithms (93,100) .…”

Section: Steady State Genetic Algorithmmentioning

confidence: 99%

Advances in Genetic Algorithm Optimization of Traffic Signals

Kesur

2009

J. Transp. Eng.

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Combining the improvements, delay reductions between 13%-32% were obtained over the standard approaches. A coding scheme allowing for complete optimization of signal phasing is proposed and a statistical model for comparing genetic algorithm optimization efficiency on stochastic functions is also introduced. Alternative delay measurements, amendments to genetic operators and modifications to the CHC algorithm are also suggested.

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The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling

et al. 1999

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The Coors warehouse scheduling problem involves finding a permutation of customer orders that minimizes the average time that customers' orders spend at the loading docks while at the same time minimizing the running average inventory, Search-based solutions require fast objective functions. Thus, a fast lowresolution simulation is used as an objective function. A slower high-resolution simulation is used to validate solutions. We compare the performance of a constructive scheduling algorithm to a genetic algorithm and local search approach. The constructive algorithm is based on a heuristic built specifically for this application. We also tested a hybrid of the genetic algorithm and local search approaches by initializing the search using the domain-specific heuristic. This hybrid genetic algorithm was able to find the best solutions when evaluated by the high-resolution simulation. Finally, we consider the effect of using the high-resolution simulation to filter a set of solutions found by the different approaches.Events do not occur at the same granularity in the two simulators. While the time units are the same in both simulators, the low-resolution simulation uses larger time steps between events than the high-resolution simulator. As a result, there will be differences in the running average inventory, I, as reported by the two simulators.-We had previously reported a 3 order of magnitude difference in the internal and external simulators [19]. The difference has been reduced to 2 orders of magnitude by using machines with considerably more memory 82 J. P. WATSON E¹ A¸. Figure 3. The external simulation values plotted against the internal simulation values for running average inventory Over the 30 runs in Figures 2 and 3, the genetic algorithm has generated 'best found' solutions that are very similar in evaluation on the internal simulator, but which display significant variation on the external simulator. This is typical behaviour for genetic algorithms when ran on noisy functions [30].When sampling a noisy function, it is very common for the signal-to-noise ratio to be such that the signal is increasingly lost in the noise as one samples better solutions that have increasingly 92 J. P. WATSON E¹ A¸.

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Searching in the presence of noise

Cited by 36 publications

References 5 publications

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Advances in Genetic Algorithm Optimization of Traffic Signals

The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling

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