Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories

Abstract

Bloat is an excess of code growth without a corresponding improvement in fitness. This is a serious problem in Genetic Programming, often leading to the stagnation of the evolutionary process. Here we provide an extensive review of all the past and current theories regarding why bloat occurs. After more than 15 years of intense research, recent work is shedding new light on what may be the real reasons for the bloat phenomenon. We then introduce Dynamic Limits, our new approach to bloat control. It implements a dynamic limit that can be raised or lowered, depending on the best solution found so far, and can be applied either to the depth or size of the programs being evolved. Four problems were used as a benchmark to study the efficiency of Dynamic Limits. The quality of the results is highly dependent on the type of limit used: depth or size. The depth variants performed very well across the set of problems studied, achieving similar fitness to the baseline technique while using significantly smaller trees. Unlike many other methods available so far, Dynamic Limits does not require specific genetic operators, modifications in fitness evaluation or different selection schemes, nor does it add any parameters to the search process. Furthermore, its implementation is simple and its efficiency does not rely on the usage of a static upper limit. The results are discussed in the context of the newest bloat theory.

Evolutionary Generation of Whole Test Suites

“…A variable size representation could lead to bloat, which is a problem that is very typical for example in Genetic Programming [23]: For instance, after each generation, test cases can become longer and longer, until all the memory is consumed, even if shorter sequences are better rewarded. Notice that bloat is an extremely complex phenomenon in evolutionary computation, and after many decades of research it is still an open problem whose dynamics and nature are not completely understood [23].…”

“…Notice that bloat is an extremely complex phenomenon in evolutionary computation, and after many decades of research it is still an open problem whose dynamics and nature are not completely understood [23].…”

“…In previous work [9], we have studied several bloat control methods from the literature of Genetic Programming [23] applied in the context of testing object oriented software. However, our previous study [9] covered only the case of targeting one branch at a time.…”

“…• We use a dynamic limit control conceptually similar to the one presented by Silva and Costa [23]. If an offspring's coverage is not better than that of the best solution T B in the current entire GA population, then it is not accepted in the new generations if it is longer than twice T B (see Line 18 in Algorithm 1).…”

Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

“…Stated more precisely, bloat is excessive code growth within the individuals of the evolving population without a proportional improvement in fitness. Over the years, many bloat theories have been developed and many bloat control methods have been proposed [31,32]. One of the most promising attempts at explaining the bloat phenomenon is the crossover bias theory [5,28], that has lead to a powerful bloat control method called operator equalisation [32].…”