A python-based design-by-contract evolutionary algorithm framework with augmented diagnostic capabilities

Evolutionary algorithms are a class of algorithms that try to mimic natural, biological evolution a la Darwinian natural selection, to compute solutions to a given problem. They are especially useful when no well known strategies for computing solutions to such a problem exist. Evolutionary algorithms begin by creating a collection (population) of candidate solutions to the problem at hand; and through repeated application of genetic operators such as crossover and mutation, they iterate over multiple generations of this population, until they eventually converge onto an attractive solution. One important problem facing code implementing Evolutionary Algorithms is that due to the dynamic nature of the individual chromosomes in a population, simple coding errors lead to complex bugs that are difficult to both diagnose and debug. This problem is only exacerbated when attempting to develop the algorithms in a dynamically typed language such as Python. This paper presents a novel Evolutionary Algorithm framework for the Python programming language that implements design-by-contract, a paradigm in which each function and class must follow a contractual set of pre-conditions and post-conditions. Failure to follow the contract causes an error condition identifying the violated clause, thereby catching bugs earlier in the development process and in a more descriptive manner.