Genetic algorithms for VLSI design, layout & test automation
Genetic algorithms for VLSI design, layout & test automation
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
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This paper presents a handshake between the concepts of genetic algorithms and the forecasting problem to present a novel search based multiphase genetic algorithm to the forecasting problem based on the time series model. The backbone concept of the paper lies in utilizing the genetic approach effectively for implementing the Autoregressive process, a linear stochastic model where a time series is supposed to be a linear aggregation of random shocks. We propose to utilize the concept of genetic algorithms to transform an initial population of random suggested solutions to a population that contains solutions approximating the optimal one. A carefully chosen fitness function acts in the capacity of a yardstick to appraise the quality of each "chromosome" to aid the selection phase. We simulated the presented approach on a Pentium IV processor and obtained results that were very encouraging.