In this work, we first introduce a novel approach to the long term irrigation scheduling
using Genetic Algorithms (GAs). We explore the effectiveness of GAs in the context of
optimizing nonlinear crop models and describe application requirements and implementation of
the technique. GAs were found to converge quickly to near-optimal solutions.
Second, we analyze the relationship between GA control parameters (population size,
crossover rate, and mutation rate) and performance. We identify a combination of population,
mutation, and crossover which searched the fitness landscape efficiently. The results suggest
that smaller populations are able to provide better performance at relatively low mutation rates.
More stable outcomes were generated using low mutation rates. Without crossover the quality of
solutions were generally impaired, and the search process was lengthened. Aside from crossover
rate zero, no other crossover rates significantly differed. The behaviors observed for best, online,
offline, and average performances were sensitive to the combined influences control parameters.
Interaction among control parameters was strongly indicated.
Finally, several adaptive penalty techniques are presented for handling constraints in
GAs, and their effectiveness is demonstrated. The constant penalty function suffered from
sensitivity to settings of penalty coefficients, and was not successful in satisfying constraints.
The adaptive penalty functions utilizes violation distance based metrics and search time based
scaling using generation or trials number, and fitness values to penalize infeasible solutions, as
the distance from the feasible region or number of generations increases so does the penalty.
They were quite successful in providing solutions with minimal effort. They adapt the penalty as
the search continues, encouraging feasible solutions to emerge over the time. Adaptive
approaches presented here are flexible, efficient, and robust to parameter settings. / Graduation date: 1997
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/34323 |
| Date | 10 April 1997 |
| Creators | Canpolat, Necati |
| Contributors | Bolte, John P. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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