The Value of Moderate Obsession: Insights from a New Model of Organizational Search

This study presents a new model of search on a “rugged landscape,” which employs modeling techniques from fractal geometry rather than the now-familiar NK modeling technique. In our simulations,firms search locally in a two-dimensional fitness landscape, choosing moves in a way that responds both to local payoff considerations and to a more global sense of opportunity represented by a firm-specific “preferred direction.” The latter concept provides a very simple device for introducing cognitive or motivational considerations into the formal account of search behavior, alongside payoff considerations. After describing the objectives and the structure of the model, we report a first experiment which explores how the ruggedness of the landscape affects the interplay of local payoff and cognitive considerations (preferred direction) in search. We show that an intermediate search strategy, combining the guidance of local search with a moderate level of non-local “obsession,” is distinctly advantageous in searching a rugged landscape. We also explore the effects of other considerations, including the objective validity of the preferred direction and the degree of dispersion of firm strategies. We conclude by noting available features of the model that are not exercised in this experiment. Given the inherent flexibility of the model, the range of questions that might potentially be explored is extremely large.Rugged Landscapes; Local Search; Cognition; Obsession; Fractal Geometry

Biomorphs via Modified Iterations

The aim of this paper is to present some modifications of the biomorphs generation algorithm introduced by Pickover in 1986. A biomorph stands for biological morphologies. It is obtained by a modified Julia set generation algorithm. The biomorph algorithm can be used in the creation of diverse and complicated forms resembling invertebrate organisms. In this paper the modifications of the biomorph algorithm in two directions are proposed. The first one uses different types of iterations (Picard, Mann, Ishikawa). The second one uses a sequence of parameters instead of one fixed parameter used in the original biomorph algorithm. Biomorphs generated by the modified algorithm are essentially different in comparison to those obtained by the standard biomorph algorithm, i.e., the algorithm with Picard iteration and one fixed constant

Evolution of Musical Organisms

The development of software for musical applications has led to a proliferation of elaborate control paradigms with extremely large parameter spaces. These spaces can be daunting to explore interactively because of their vastness. Furthermore, parameters often interact in ways not made explicit by the control panel, effectively increasing the complexity of the space even further. Application of genetic algorithms (GAs) can be used to search through these vast spaces in a highly efficient manner. Coordinated control of interacting parameters is handled automatically by this system. Even for control paradigms that are well understood, the genetic algorithm can efficiently search out control settings that would have been otherwise unlikely to arise. The author has developed a software system that employs genetic algorithms to evolve \u27musical organisms\u27. The system is built on MidiForth, the author\u27s computer-assisted composition software [Degazio 1988, 1993] and employs many unique functions developed in previous research. This paper describes the second phase of research; future work will extend the GA searching technique to abstract, subjective musical concepts such as density and smoothness

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance

Synthetic Aesthetics

“Synthetic Aesthetics” is a phrase constructed on the lexical schemes of “Informational Aesthetics” and “Synthetic Phenomenology”, the research on artificial systems that possess or specify phenomenal states. It denotes the research on the cognitive capacities required for the production and the evaluation of Art through computational modelling and simulation

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance

An investigation into Quadtree fractal image and video compression

Digital imaging is the representation of drawings, photographs and pictures in a format that can be displayed and manipulated using a conventional computer. Digital imaging has enjoyed increasing popularity over recent years, with the explosion of digital photography, the Internet and graphics-intensive applications and games. Digitised images, like other digital media, require a relatively large amount of storage space. These storage requirements can become problematic as demands for higher resolution images increases and the resolution capabilities of digital cameras improve. It is not uncommon for a personal computer user to have a collection of thousands of digital images, mainly photographs, whilst the Internet’s Web pages present a practically infinite source. These two factors 一 image size and abundance 一 inevitably lead to a storage problem. As with other large files, data compression can help reduce these storage requirements. Data compression aims to reduce the overall storage requirements for a file by minimising redundancy. The most popular image compression method, JPEG, can reduce the storage requirements for a photographic image by a factor of ten whilst maintaining the appearance of the original image 一 or can deliver much greater levels of compression with a slight loss of quality as a trade-off. Whilst JPEG's efficiency has made it the definitive image compression algorithm, there is always a demand for even greater levels of compression and as a result new image compression techniques are constantly being explored. One such technique utilises the unique properties of Fractals. Fractals are relatively small mathematical formulae that can be used to generate abstract and often colourful images with infinite levels of detail. This property is of interest in the area of image compression because a detailed, high-resolution image can be represented by a few thousand bytes of formulae and coefficients rather than the more typical multi-megabyte filesizes. The real challenge associated with Fractal image compression is to determine the correct set of formulae and coefficients to represent the image a user is trying to compress; it is trivial to produce an image from a given formula but it is much, much harder to produce a formula from a given image. เท theory, Fractal compression can outperform JPEG for a given image and quality level, if the appropiate formulae can be determined. Fractal image compression can also be applied to digital video sequences, which are typically represented by a long series of digital images 一 or 'frames'

Branching Boogaloo: Botanical Adventures in Multi-Mediated Morphologies

FormaLeaf is a software interface for exploring leaf morphology using parallel string rewriting grammars called L-systems. Scanned images of dicotyledonous angiosperm leaves removed from plants around Bard’s campus are displayed on the left and analyzed using the computer vision library OpenCV. Morphometrical information and terminological labels are reported in a side-panel. “Slider mode” allows the user to control the structural template and growth parameters of the generated L-system leaf displayed on the right. “Vision mode” shows the input and generated leaves as the computer ‘sees’ them. “Search mode” attempts to automatically produce a formally defined graphical representation of the input by evaluating the visual similarity of a generated pool of candidate leaves. The system seeks to derive a possible internal structural configuration for venation based purely off a visual analysis of external shape. The iterations of the generated L-system leaves when viewed in succession appear as a hypothetical development sequence. FormaLeaf was written in Processing