Rapid advances in integration technology have tremendously increased the design complexity of very large scale integrated (VLSI) circuits, necessitating robust optimization techniques in many stages of VLSI design. A genetic algorithm (GA) is a stochastic optimization technique that uses principles derived from the evolutionary process in nature. In this work, genetic algorithms are used to alleviate the hardware design process of VLSI application specific integrated circuits (ASICs) and reconfigurable hardware. VLSI ASIC design suffers from high design complexity and a large number of optimization objectives requiring hierarchical design approaches and multi-objective optimization techniques. The floorplanning stage of the design cycle becomes highly important in hierarchical design methods. In this work, a multi-objective genetic algorithm based floorplanner has been developed with novel crossover operators to address the multi-objective floorplanning problem for VLSI ASICs. The genetic floorplanner achieves significant wirelength savings (>19% on average) with little or no increase in area ( < 3% penalty) over previous floorplanners that perform simultaneous area and wirelength minimization. Hardware implementation of genetic algorithms is gaining importance because of their proven effectiveness as optimization engines for real-time applications. Earlier hardware implementations suffer from major drawbacks such as absence of GA parameter programmability, rigid pre-defined system architecture, and lack of support for multiple fitness functions. A compact IP core that implements a general purpose GA engine has been designed to realize evolvable hardware in field programmable gate array devices. The designed GA core achieved a speedup of around 5.16x over an analogous software implementation. Novel reconfigurable analog architectures have been proposed to realize extreme environment analog electronics. In this work, a digital framework has been developed to realize self reconfigurable analog arrays (SRAA) where genetic algorithms are used to evolve the required analog functionality and compensate performance degradation in extreme environments. The framework supports two methods of compensation, namely, model based lookup and genetic algorithm based compensation and is scalable in terms of the number of fitness evaluation modules. The entire framework has been implemented as a digital ASIC in a leading industrystrength silicon-on-insulator (SOI) technology to obtain high performance and a small form factor.