Using Genetic Algorithms to Develop Investment Strategies

Genetic algorithms (GAs) are a powerful search technique. The use of genetic algorithms (GAs) will help in the development of better trading systems. The genetic algorithms (GAs) help the researcher to explore various combinations of trading rules or their parameters, which the human mind is unable to find. This chapter explains genetic algorithms (GAs) in brief and gives insight on how they find better trading strategies. Some of the manual trading strategies are good in nature. Genetic algorithms (GAs) only addition to them. Interfacing genetic algorithms (GAs) with stock trading systems or developing a combined model requires a large degree of imagination and creativity. It is an art not a scientific invention. Genetic algorithms (GAs) make use of computers to find various interesting trading systems.

Hacking nature: genetic tools for reprograming enzymes

Enzymes have many modern industrial applications, from biomass decomposition in the production of biofuels to highly stereospecific biotransformations in pharmaceutical manufacture. The capacity to find or engineer enzymes with activities pertinent to specific applications has been essential for the growth of a multibillion dollar enzyme industry. Over the course of the past 50–60 years our capacity to address this issue has become increasingly sophisticated, supported by innumerable advances, from early discoveries such as the co-linearity of DNA and protein sequence1 to modern computational technologies for enzyme design. The design of enzyme function is an exciting nexus of fundamental biochemical understanding and applied engineering. Herein, we will cover some of the methods used in discovery and design, including some ‘next generation’ tools.