Most efforts in Computer Vision focus on natural images or artwork, which differ significantly both in size and contents from the kind of data biomedical image processing deals with. Thus, Transfer Learning models often prove themselves suboptimal for these tasks, even after manual finetuning. The development of architectures from scratch is oftentimes unfeasible due to the vastness of the hyperparameter space and a shortage of time, computational resources and Deep Learning experts in most biomedical research laboratories. An alternative to manually defining the models is the use of Neuroevolution, which employs metaheuristic techniques to optimize Deep Learning architectures. However, many algorithms proposed in the neuroevolutive literature are either too unreliable or limited to a small, predefined region of the hyperparameter space. To overcome these shortcomings, we propose the Chimera Algorithm, a novel, hybrid neuroevolutive algorithm that integrates the Artificial Bee Colony Algorithm with Evolutionary Computation tools to generate models from scratch, as well as to refine a given previous architecture to better fit the task at hand. The Chimera Algorithm has been validated with two datasets of natural and medical images, producing models that surpassed the performance of those coming from Transfer Learning.

Data mining focuses on discovering interesting, non-trivial and meaningful information from large datasets. Data clustering is one of the unsupervised and descriptive data mining task which group data based on similarity features and physically stored together. As a partitioning clustering method, K-means is widely used due to its simplicity and easiness of implementation. But this method has limitations such as local optimal convergence and initial point sensibility. Due to these impediments, nature inspired Swarm based algorithms such as Artificial Bee Colony Algorithm, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm and etc. are used for data clustering to cope with larger datasets with lack and inconsistency of data. In some cases, those algorithms are used with traditional approaches such as K-means as hybrid approaches to produce better results. This paper reviews the performances of these new approaches and compares which is best for certain problematic situation.

Artificial Bee Colony (ABC) algorithm is a Swarm Intelligence optimization algorithm inspired by the functioning of honey bees trying to find the best nectar resources surrounding their bee hive. Derviş Kara-Bogaz first proposed this algorithm in 2005. This algorithm has been used in many forms of optimization of complex non-linear functions. As you will see soon, this algorithm is dependent on the randomness of the situation, it is a great domain for applying better strategies to find the optimal point even faster. You will also notice that if the algorithm has a hint that the point is somehow a local minimum, it has a strategy to even discard it.

Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of a honey bee swarm. We'll be looking at the ABC algorithm in detail through its purpose, implementation, and functionality. We will then solve a few problems optimizing benchmark functions such as Sphere, Himmelblau, and the Cross-In-Tray function shown below. We will also look at the application of the ABC Algorithm to real-world business problems. Full, reusable code for the implementation is available on Github. At AAXIS Digital, we routinely encounter intractable business optimization problems that require out-of-the-box thinking. To model these business problems to be solved computationally, we need to model it as a list of decision variables representing a candidate solution and be able to compute a "measure of goodness", called the objective function.

As an effective algorithm for solving complex optimization problems, artificial bee colony (ABC) algorithm has shown to be competitive, but the same as other population-based algorithms, it is poor at balancing the abilities of global searching in the whole solution space (named as exploration) and quick searching in local solution space which is defined as exploitation. For improving the performance of ABC, an adaptive group collaborative ABC (AgABC) algorithm is introduced where the population in different phases is divided to specific groups and different search strategies with different abilities are assigned to the members in groups, and the member or strategy which obtains the best solution will be employed for further searching. Experimental results on benchmark functions show that the proposed algorithm with dynamic mechanism is superior to other algorithms in searching accuracy and stability. Furthermore, numerical experiments show that the proposed method can generate the optimal solution for the complex scheduling problem.

The Artificial Bee Colony (ABC) algorithm is an evolutionary optimization algorithm based on swarm intelligence and inspired by the honey bees' food search behavior. Since the ABC algorithm has been developed to achieve optimal solutions by searching in the continuous search space, modification is required to apply this method to binary optimization problems. In this paper, we improve the ABC algorithm to solve binary optimization problems and call it the improved binary Artificial Bee Colony (ibinABC). The proposed method consists of an update mechanism based on fitness values and processing different number of decision variables. Thus, we aim to prevent the ABC algorithm from getting stuck in a local minimum by increasing its exploration ability. We compare the ibinABC algorithm with three variants of the ABC and other meta-heuristic algorithms in the literature. For comparison, we use the wellknown OR-Library dataset containing 15 problem instances prepared for the uncapacitated facility location problem. Computational results show that the proposed method is superior to other methods in terms of convergence speed and robustness. The source code of the algorithm will be available on GitHub after reviewing process

Data analysis aims at extracting and modeling information content to identify patterns within the data. As a manner of simplifying the amount of information to describe a large set of data, features are extracted from the data, serving as representative characteristics of its contents. In image analysis, for instance, examples of features include color, texture, edges, object shape, interest points, among others. These features usually are organized into an n-dimensional feature vector. Feature selection is an important step used in several tasks, such as image classification, cluster analysis, data mining, pattern recognition, image retrieval, among others. It is a crucial preprocessing technique for effective data analysis, where only a subset from the original data features is chosen to eliminate noisy, irrelevant or redundant features.

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.

Artificial bee colony (ABC), an optimization algorithm is a recent addition to the family of population based search algorithm. ABC has taken its inspiration from the collective intelligent foraging behavior of honey bees. In this study we have incorporated golden section search mechanism in the structure of basic ABC to improve the global convergence and prevent to stick on a local solution. The proposed variant is termed as ILS-ABC. Comparative numerical results with the state-of-art algorithms show the performance of the proposal when applied to the set of unconstrained engineering design problems. The simulated results show that the proposed variant can be successfully applied to solve real life problems.

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.