Genetic algorithms for VLSI design, layout & test automation
Genetic algorithms for VLSI design, layout & test automation
Dynamics of complex systems
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Design by Optimization in Architecture, Building, and Construction
Design by Optimization in Architecture, Building, and Construction
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This paper implores the possible intervention of computers in the generative (concept) stage of settlement planning. The objective was to capture the complexity and character of naturally grown fishing settlements through simple rules and incorporate them in the process of design. A design tool was developed for this purpose. This design tool used a generative evolutionary design technique, which is based on multidisciplinary methods. Facets of designing addressed in this research are:*allocation of each design element's space and geometry, *defining the rules, constraints and relationships governing the elements of design, *the purposeful search for better alternative solutions, *quantitative evaluation of the solution based on spatial, comfort, complexity criterions to ensure the needed complexity, usability in the solutions. Generative design methods such as geometric optimization, shape grammars and genetic algorithms have been combined for achieving the above purposes. The allocation of space has been achieved by geometric optimization techniques, which allocate spaces by proliferation of a simple shape unit. This research conducts an analysis of various naturally grown fishing settlements and identifies the features that would be essential to recreate such an environment. Features such as the essential elements, their relationships, hierarchy, and order in the settlement pattern, which resulted due to the occupational and cultural demands of the fisher folk, are analysed. The random but ordered growth of the settlement is captured as rules and relations. These rules propel and guide the whole process of design generation. These rules and certain constraints, restrictions control the random arrangement of the shape units. This research limits itself to conducting exhaustive search in the prescribed solution search space defined a priori by the rules and relationships. This search within a bounded space can be compared to the purposeful, constrained decision making process involved in designing. The generated solutions use the evolutionary concept of genetic algorithms to deduce solutions within the predefined design solution search space. Simple evolutionary concepts such as reproduction, crossover and mutation aid this search process. These concepts transform by swapping/interchanging the genetic properties (the constituent data/material making up the solution) of two generated solutions to produce alternate solutions. Thus the genetic algorithm finds a series of new solutions. With such a tool in hand various possibilities of design solutions could be analysed and compared. A thorough search of possible solutions ensures a deeper probe essential for a good design. The spatial quality, comfort quality of the solutions are compared and graded (fitness value) against the standard stipulations. These parameters look at the solution in the context of the whole and not as parts and most of these parameters could be improved only at the expense of another. The tool is able to produce multiple equally good solutions to the same problem, possibly with one candidate solution optimizing one parameter and another candidate optimizing a different one. The final choice of the suitable solution is made based on the user's preferences and objectives. The tool is tested for an existing fishing settlement. This was done to check for its credibility and to see if better alternatives evolved. The existing settlement is analysed based on the evaluation parameters used in the tool and compared with the generated solutions. The results of the tool has proved that simple rules when applied recursively within constraints would provide solutions that are unpredictable and also would resonate the qualities of the knowledge from which the rules were distilled from. The complex whole generated has often exhibited emergent properties and thus opens up new avenues of thinking.