• Genetic algorithms

    I have previously been studying a whole bunch of nature inspired algorithms, or in other words optimisation methods inspired by natural phenomena. In this article, I’ll cover the basics of creating a genetic algorithm (GA). The nature of evolutionary algorithms A GA is an evolutionary algorithm. Evolutionary algorithms are inspired by the mechanisms of biological evolution by means of natural selection (as proposed by Darwin). The mechanisms of biological evolution revolves around modification and propagation of genetic material over time. Parents contribute with genetic material to create an offspring, and along the way there is some probability of natural mutation. In layman’s terms Basically, a GA mimics the evolution of some…

  • Simulated annealing

    I’m currently studying a whole bunch of nature inspired algorithms, or in other words optimisation methods inspired by natural phenomena. The simulated annealing (SA) algorithm is an optimisation method that is able to also solve complex optimisation problems. The strength of SA is its ability to overcome the ‘neighbourhood limit’ that restricts simple optimisation algorithms. A classical example of the applicability of SA, also emphasised in the original article Optimization by Simulated Annealing (Kirkpatrick et al. 1983), is its ability to solve the traveling salesman problem (a well known NP problem where we want to optimise a travel person’s path, so (s)he travels the least amount of miles). Optimisation algorithms in general…