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Downloaded Fbuilding design can reduce the cost and time required to home in on desirable design strategies. Genetic algorithms ~GAs! are an optimization method that has been applied to many types of opti-mization problems associated with buildings. This paper first reviews the basics of GAs, describes the wide range of applications of GAs, and identifies their advantages rela-tive to other optimization methods. It then describes the use of GAs for multi-objective optimization. Next, it focuses on the use of GAs in three areas: optimization of the building envelope to minimize HVAC and lighting energy and construction costs; op-timization of building form; and optimization of the design and operation of HVAC systems. Finally, conclusions are drawn. Genetic Algorithms–How and Why? GA Basics. A genetic algorithm is a technique used to auto-mate the process of searching for an optimal solution. Because it searches from a population of points, the probability of the searchs getting trapped in a local minimum is limited. GAs start involves randomly changing an allele in a solution to look for new points in the solution space. Although there are more elaborate versions of these operators, the basic principles remain similar for most GAs. A genetic algorithm starts by generating a number of possible solutions to a problem, evaluates them and applies the basic ge-netic operators to that initial population according to the indi-vidual fitness of each individual. This process generates a new population with higher average fitness than the previous one, which in turn will be evaluated. The cycle is repeated for the number of generations set by the user, which is dependent on problem complexity. Types of Problems Solved With GAs. GAs have been ap-plied to an extremely wide range of problems in engineering sys-tems since their widespread introduction in 198