Exact computation of the expectation surfaces for uniform crossover along with bit-flip mutation

Theoretical Computer Science 545, 2014, pp.76-93,Uniform crossover and bit-flip mutation are two popular operators used in genetic algorithms to generate new solutions in an iteration of the algorithm when the solutions are represented by binary strings. We use the Walsh decomposition of pseudo-Boolean functions and properties of Krawtchouk matrices to exactly compute the expected value for the fitness of a child generated by uniform crossover followed by bit-flip mutation from two parent solutions. We prove that this expectation is a polynomial in ρ, the probability of selecting the best-parent bit in the crossover, and μ, the probability of flipping a bit in the mutation. We provide efficient algorithms to compute this polynomial for Onemax and MAX-SAT problems, but the results also hold for other problems such as NK-Landscapes. We also analyze the features of the expectation surfaces.Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194 (the roadME project). Air Force Office of Scientific Research, Air Force Materiel Command, USAF, under grant number FA9550-11-1-0088

Optimisation of image processing networks for neuronal membrane detection

This research dealt with the problem of neuronal membrane detection, in which the core challenge is distinguishing membranes from organelles. A simple and efficient optimisation framework is proposed based on several basic processing steps, including local contrast enhancement, denoising, thresholding, hole-filling, watershed segmentation, and morphological operations. The two main algorithms proposed Image Processing Chain Optimisation (IPCO) and Multiple IPCO (MIPCO)combine elements of Genetic Algorithms, Differential Evolution, and Rank-based uniform crossover. 91.67% is the highest recorded individual IPCO score with a speed of 280 s, and 92.11% is the highest recorded ensembles IPCO score whereas 91.80% is the highest recorded individual MIPCO score with a speed of 540 s for typically less than 500 optimisation generations and 92.63% is the highest recorded ensembles MIPCO score.Further, IPCO chains and MIPCO networks do not require specialised hardware and they are easy to use and deploy. This is the first application of this approach in the context of the Drosophila first instar larva ventral nerve cord. Both algorithms use existing image processing functions, but optimise the way in which they are configured and combined. The approach differs from related work in terms of the set of functions used, the parameterisations allowed, the optimisation methods adopted, the combination framework, and the testing and analyses conducted. Both IPCO and MIPCO are efficient and interpretable, and facilitate the generation of new insights. Systematic analyses of the statistics of optimised chains were conducted using 30 microscopy slices with corresponding ground truth. This process revealed several interesting and unconventional insights pertaining to preprocessing, classification, post-processing, and speed, and the appearance of functions in unorthodox positions in image processing chains, suggesting new sets of pipelines for image processing. One such insight revealed that, at least in the context of our membrane detection data, it is typically better to enhance, and even classify, data before denoising them

Optimisation of image processing networks for neuronal membrane detection

This research dealt with the problem of neuronal membrane detection, in which the core challenge is distinguishing membranes from organelles. A simple and efficient optimisation framework is proposed based on several basic processing steps, including local contrast enhancement, denoising, thresholding, hole-filling, watershed segmentation, and morphological operations. The two main algorithms proposed Image Processing Chain Optimisation (IPCO) and Multiple IPCO (MIPCO)combine elements of Genetic Algorithms, Differential Evolution, and Rank-based uniform crossover. 91.67% is the highest recorded individual IPCO score with a speed of 280 s, and 92.11% is the highest recorded ensembles IPCO score whereas 91.80% is the highest recorded individual MIPCO score with a speed of 540 s for typically less than 500 optimisation generations and 92.63% is the highest recorded ensembles MIPCO score.Further, IPCO chains and MIPCO networks do not require specialised hardware and they are easy to use and deploy. This is the first application of this approach in the context of the Drosophila first instar larva ventral nerve cord. Both algorithms use existing image processing functions, but optimise the way in which they are configured and combined. The approach differs from related work in terms of the set of functions used, the parameterisations allowed, the optimisation methods adopted, the combination framework, and the testing and analyses conducted. Both IPCO and MIPCO are efficient and interpretable, and facilitate the generation of new insights. Systematic analyses of the statistics of optimised chains were conducted using 30 microscopy slices with corresponding ground truth. This process revealed several interesting and unconventional insights pertaining to preprocessing, classification, post-processing, and speed, and the appearance of functions in unorthodox positions in image processing chains, suggesting new sets of pipelines for image processing. One such insight revealed that, at least in the context of our membrane detection data, it is typically better to enhance, and even classify, data before denoising them