Optimization of truss-structures for finding optimal cross-sectional size, topology, and configuration of 2-D and 3-D trusses to achieve minimum weight is carried out using real-coded genetic algorithms (GAs). All the above three optimization techniques have been made possible by using a novel representation scheme. Although the proposed GA uses a fixed-length vector of design variables represe...

Absract This chapter intends to present a brief review of genetic search algorithms and introduce a new type of genetic algorithms (GAs) called the real coded structural genetic algorithm (RSGA) for function optimization. The new genetic model combines the advantages of traditional real genetic algorithm (RGA) with structured genetic algorithm (SGA). This specific feature makes it able to solve...

In this paper a decision making scheme devised by Lobo and Goldberg for a hybrid genetic algorithm is extended to deal with the problem of adaptation of the operator probabilities of a real coded steady-state genetic algorithm applied to optimization problems. The scheme is modi ed by introducing a global reference value for measuring operator productivity as well as by the inclusion of operato...

Genetic algorithms are efficient global optimizers, but they are weak in performing fine-grained local searches. In this paper, the local search capability of genetic algorithm is improved by hybridizing real coded genetic algorithm with 'uniform random' local search to form a hybrid real coded genetic algorithm termed 'RCGAu'. The incorporated local technique is applied to all newly created of...

In this paper, a exible yet eecient algorithm for solving engineering design optimization problems is presented. The algorithm is developed based on both binary-coded and real-coded genetic algorithms (GAs). Since both GAs are used, the variables involving discrete, continuous, and zero-one variables are handled quite eeciently. The algorithm restricts its search only to the permissible values ...

Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used in binary-coded GAs. How...

This pap er present s a theory of convergence for realcoded genet ic algorit hmsGAs that use float ing-point or oth er highcardinality codings in their chromosomes . The theory is consistent with th e theory of schemata and postulates tha t select ion domin ates early GA perform ance and restri cts subsequent search to int ervals with above-average fun ction values, dimension-by-dimension. Thes...

Binary-coded genetic algorithms (BGAs) traditionally use a uniform mapping to decode strings to corresponding real-parameter variable values. In this paper, we suggest a non-uniform mapping scheme for creating solutions towards better regions in the search space, dictated by BGA’s population statistics. Both variable-wise and vector-wise non-uniform mapping schemes are suggested. Results on fiv...

Deformable models are by their formulation able to solve surface extraction problem from noisy volumetric image data encountered commonly in medical image analysis. However, this ability is shadowed by the fact that the minimization problem formulated is difficult to solve globally. Constrained global solutions are needed, if the amount of noise is substantial. This paper presents a new optimiz...

In this paper, a set of new Real-Coded Genetic Algorithms (RCGAs) with local and global exploratory search capabilities are proposed. The search capabilities are based on the inclusion of a modified crossover procedure and a new global exploratory method in RCGA. The global exploratory method is based on vector projection while the modified crossover procedure is based on a limited version of t...