Genetic Evolutionary Programming Research Articles

Genetic Programming을 이용한 코너 검출자의 자동생성

본 논문은 영상처리에 사용되는 코너점 추출을 위한 GP(Genetic Programming)기반의 코너 검출자를 소개한다. Harris와 SUSAN등 기존의 대표적인 코너 검출자들이 소개되어 왔고, 여러 가지 경험적인 알고리즘들이 연산 시간과 정확도 측면에서 이들 기법을 개선하기 위해서 연구되어 오고 있다. 이들 기법들은 코너점에 대한 특성을 고찰하여 이를 알고리즘화한 것으로 효율성이 높으나, 한편으로 기존의 방식이나 알고리즘에서 크게 벗어난 혁신적인 알고리즘을 발견하기에는 한계가 있다. 본 연구에서는 GP의 진화연산에 의해 자동적으로 코너 검출자를 생성함으로서 새로운 기법의 가능성을 발견하고자 한다. 제안된 방법을 다른 코너 검출자들과 테스트영상을 통해 비교 분석 하였다. This paper introduces GP(Genetic Programming) based corner detectors for an image processing. Various empirical algorithms have been studied to improve computational speed and accuracy including typical approaches, such as Harris and SUSAN. The these techniques are highly efficient, because properties of corner points are inspected and reflected into the algorithms. However these approaches are limited in discovering an innovative algorithm. In this study, we try to discover a more efficient technique by creating corner detector automatically using evolution of GP. The proposed method is compared to the existing corner detectors for test images.

Linear-in-Parameter Models Based on Parsimonious Genetic Programming Algorithm and Its Application to Aero-Engine Start Modeling

A novel Parsimonious Genetic Programming (PGP) algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming(GP) is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm (OLS) is used to estimate the contribution of the branches of the tree (refer to basic function term that cannot be decomposed anymore according to special rule) to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP's convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines (SVM) validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.

Genetic perceptual shaping: Utilizing cover image and conceivable attack information during watermark embedding

We describe a new watermarking scheme based on intelligent shaping of a digital watermark using Genetic Programming (GP). The proposed method, in addition to achieving a superior tradeoff between watermark robustness and imperceptibility, is also able to structure the watermark in accordance with an anticipated attack. This has been achieved by simultaneously hiding the watermark as well as spreading and fusing it in such a way to resist the conceivable attack. Robustness versus imperceptibility tradeoff and increase in bit correct ratio after attack, have been employed as the optimization criteria in the GP search. The concept of bonus fitness has been used to implement multi-objective fitness based GP evolution. Experiments on standard images indicate that such watermark shaping functions could be developed that are cover image independent and enhance imperceptibility. They offer high resistance against removal and interference attacks of Checkmark benchmark.

Multi-step optimal control of complex process: a genetic programming strategy and its application

In many industrial processes, especially chemistry and metallurgy industry, the plant is slow for feedback and data test because of complex and varying factors. Considering the multi-objective feature and the complex problem of production stability in optimal control, this paper proposed an optimal control strategy based on genetic programming (GP), used as a multi-step state transferring procedure. The fitness function is computed by multi-step comprehensive evaluation algorithm, which provides a synthetic evaluation of multi-objective in process state based on single objective models. The punishment to process state variance is also introduced for the balance between optimal performance and stability of production. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution in GP with compact individuals. The optimal solution gained by evolution is a multi-step command program of process control, which not only ensures the optimization tendency but also avoids violent process variation by adjusting control parameters step by step. An optimal control system for operation direction is developed based on this strategy for imperial smelting process in Shaoguan. The simulation and application results showed its effectiveness for production objects optimization in complex process control.

MULTI-AGENT-BASED MODELING OF ARTIFICIAL STOCK MARKETS BY USING THE CO-EVOLUTIONARY GP APPROACH

This paper deals with multi-agent based modeling of artificial stock market by using the co- evolutionary Genetic Programming (GP) by considering social learning. Cognitive behaviors of agents are modeled by using the GP to introduce social learning as well as individual learning. Assuming five types of agents, in which rational agents prefer forecast models (equations) or production rules to support their decision making, and irrational agents select decisions at random like a speculator. Rational agents usually use their own knowledge base, but some of them utilize their public (common) knowledge base to improve trading decisions. By using the result of simulation studies on artificial market, it is shown that the time series for stock price is resemble to real stock price statistically. It is also shown that the lack of social learning leads the system to a very monotone market, and only a simple behavior of the market is realized. Moreover, we can see the effectiveness of classifier systems where we utilize a pool of decision rules in which not only prominent but also rules having potential rewards in fluctuating environment. It is also seen that the growth of wealth of irrational agent is almost always better than rational agents even though they analyze and behaves on reasonable decision. The result provide us the way to analyze real market where traders usually use social learning and environment-dependent rules.

Identification of an urban fractured-rock aquifer dynamics using an evolutionary self-organizing modelling

An urban fractured-rock aquifer system, where disposal of storm water is via ‘soak holes’ drilled directly into the top of fractured-rock basalt, has a highly dynamic nature where theories or knowledge to generate the model are still incomplete and insufficient. Therefore, formulating an accurate mechanistic model, usually based on first principles (physical and chemical laws, mass balance, and diffusion and transport, etc.), requires time- and money-consuming tasks. Instead of a human developing the mechanistic-based model, this paper presents an approach to automatic model evolution in genetic programming (GP) to model dynamic behaviour of groundwater level fluctuations affected by storm water infiltration. This GP evolves mathematical models automatically that have an understandable structure using function tree representation by methods of natural selection (‘survival of the fittest’) through genetic operators (reproduction, crossover, and mutation). The simulation results have shown that GP is not only capable of predicting the groundwater level fluctuation due to storm water infiltration but also provides insight into the dynamic behaviour of a partially known urban fractured-rock aquifer system by allowing knowledge extraction of the evolved models. Our results show that GP can work as a cost-effective modelling tool, enabling us to create prototype models quickly and inexpensively and assists us in developing accurate models in less time, even if we have limited experience and incomplete knowledge for an urban fractured-rock aquifer system affected by storm water infiltration.