Summary.The social foraging behavior of Escherichia coli (E. Coli) bacteria has been used to solve optimization problems. This chapter proposes a hybrid approach involving genetic algorithm (GA) and bacterial foraging (BF) algorithm for function optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the lifetime of the bacteria. The proposed algorithm is then used to tune a PID controller of an Automatic Voltage Regulator (AVR). To design disturbance rejection tuning, disturbance rejection conditions based on H ∞ are illustrated and the performance of response is computed for the designed PID controller as the integral of time weighted squared error. Simulation results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems. IntroductionIn the last decade, approaches based on genetic algorithms (GA) have received increased attention from the academic and industrial communities for dealing with optimization problems that have been shown to be intractable using conventional problem solving techniques.In the past, some researchers have focused on using hybrid genetic algorithm approaches for optimization problems. Buczak and Uhrig [1] proposed a novel hierarchical fuzzy-genetic information fusion technique. The combined reasoning takes place by means of fuzzy aggregation functions, capable of combining information by compensatory connectives that better mimic the human reasoning process than union and intersection, employed in traditional set theories. The parameters of the connectives are found by genetic algorithms. evaluated the use of different methods from the fuzzy modeling field for classification tasks and the potential of their integration in producing better classification results. The methods considered, approximate in nature, study the integration of techniques with an initial rule generation step and a following rule tuning approach using different evolutionary algorithms.