Detection of Intrusions and Malware, and Vulnerability Assessment

Several methods to incorporate semantic awareness in genetic programming have been proposed in the last few years. These methods cover fundamental parts of the evolutionary process: from the population initialization, through different ways of modifying or extending the existing genetic operators, to formal methods, until the definition of completely new genetic operators. The objectives are also distinct: from the maintenance of semantic diversity to the study of semantic locality; from the use of semantics for constructing solutions which obey certain constraints to the exploitation of the geometry of the semantic topological space aimed at defining easy-to-search fitness landscapes. All these approaches have shown, in different ways and amounts, that incorporating semantic awareness may help improving the power of genetic programming. This survey analyzes and discusses the state of the art in the field, organizing the existing methods into different categories. It restricts itself to studies where semantics is intended as the set of output values of a program on the training data, a definition that is common to a rather large set of recent contributions. It does not discuss methods for incorporating semantic information into grammar-based genetic programming or approaches based on formal methods. The objective is keeping the community updated on this interesting research track, hoping to motivate new and stimulating contributions.

A survey of semantic methods in genetic programming

A Taxonomic Review of Metaheuristic Algorithms for Solving the Vehicle Routing Problem and Its Variants

In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the slowest of the available methodologies. We discuss the results in detail and point to future research directions that may allow symbolic regression to gain wider adoption in the machine learning community.

Where are we now? A large benchmark study of recent symbolic regression methods

Statistical genetic programming for symbolic regression

The semantic geometric crossover (SGX) proposed by Moraglio et al. has achieved very promising results and received great attention from researchers, but has a significant disadvantage in the exponential growth in size of the solutions. We propose a crossover operator named subtree semantic geometric crossover (SSGX), with the aim of addressing this issue. It is similar to SGX but uses subtree semantic similarity to approximate the geometric property. We compare SSGX to standard crossover (SC), to SGX, and to other recent semantic-based crossover operators, testing on several symbolic regression problems. Overall our new operator out-performs the other operators on test data performance, and reduces computational time relative to most of them. Further analysis shows that while SGX is rather exploitative, and SC rather explorative, SSGX achieves a balance between the two. A simple method of further enhancing SSGX performance is also demonstrated.

Subtree semantic geometric crossover for genetic programming

Solving the multi-vehicle multi-covering tour problem

Population diversity has long been seen as a crucial factor for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular. This paper experimentally investigates the diversity property of a recently proposed crossover, Semantic Similarity based Crossover (SSC). The results show that while SSC helps to improve locality, it leads to the loss of diversity of the population. This could be the reason that sometimes SSC fails in achieving superior performance when compared to standard subtree crossover. Consequently, we introduce an approach to maintain the population diversity by combining SSC with a multi-population approach. The experimental results show that this combination maintains better population diversity, leading to further improvement in GP performance. Further SSC parameters tuning to promote diversity gains even better results.

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Examining the diversity property of semantic similarity based crossover

Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followe...

Arc Routing with Time-Dependent Travel Times and Paths

Phishing is a real threat on the Internet nowadays. According to a report released by an American security firm, RSA, there have been approximately 33,000 phishing attacks globally each month in 2012, leading to a loss of $687 million. Therefore, fighting against phishing attacks is of great importance. One popular and widely-deployed solution with browsers is to integrate a blacklist sites into them. However, this solution, which is unable to detect new attacks if the database is out of date, appears to be not effective when there are a lager number of phishing attacks created very day. In this paper, we propose a solution to this problem by applying Genetic Programming to phishing detection problem. We conducted the experiments on a data set including both phishing and legitimate sites collected from the Internet. We compared the performance of Genetic Programming with a number of other machine learning techniques and the results showed that Genetic Programming produced the best solutions to phishing detection problem.

Tuan Anh Pham

Papers

Subtree semantic geometric crossover for genetic programming

Solving the multi-vehicle multi-covering tour problem

Examining the diversity property of semantic similarity based crossover

Arc Routing with Time-Dependent Travel Times and Paths

Phishing Attacks Detection Using Genetic Programming