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This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

The purpose of this paper is to implement a Web interface for hybrid intelligent systems. Using the implemented Web interface, this paper evaluates two hybrid intelligent systems based on particle swarm optimization, hill climbing and distributed genetic algorithm to solve the node placement problem in wireless mesh networks (WMNs).

The node placement problem in WMNs is well-known to be a computationally hard problem. Therefore, the authors use intelligent algorithms to solve this problem. The implemented systems are intelligent systems based on meta-heuristics algorithms: Particle Swarm Optimization (PSO), Hill Climbing (HC) and Distributed Genetic Algorithm (DGA). The authors implement two hybrid intelligent systems: WMN-PSODGA and WMN-PSOHC-DGA.

The authors carried out simulations using the implemented Web interface. From the simulations results, it was found that the WMN-PSOHC-DGA system has a better performance compared with the WMN-PSODGA system.

For simulations, the authors considered Normal distribution of mesh clients. In the future, the authors need to consider different client distributions, patterns, number of mesh nodes and communication distance.

In this research work, the authors implemented a Web interface for hybrid intelligent systems. The implemented interface can be extended for other metaheuristic algorithms.

The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems.

Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages.

Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches.

The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

Education is increasingly using Intelligent Tutoring Systems (ITS), both for modelling instructional and teaching strategies and for enhancing educational programs. The first part of the paper introduces the basic structure of an ITS as well as common problems being experienced within the ITS community. The second part describes WITNeSS ‐ an original hybrid intelligent system using Fuzzy‐Neural‐GA techniques for optimising the presentation of learning material to a student. The original work in this paper is related to the concept of a “virtual student”. This student model, modelled using fuzzy technologies, will be useful for any ITS, providing it with an optimal learning strategy for fitting the ITS itself to the unique needs of each individual student. In the third part, experiments focus on problems developing a “virtual student” model, which simulates, in a rudimentary way, human learning behaviour. Part four finishes with concluding remarks.

In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data and the classification system that introduces the appropriate size from the sizing chart to each person. To solve this problem, as a first study in the literature, a hybrid intelligent classification model as a size recommendation expert system is proposed. The paper aims to discuss these issues.

Three stages for developing a hybrid intelligent classification system based on data clustering and probabilistic neural network (PNN) are proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference for developing a new intelligent classification system by using a PNN. At the last stage, the accuracy of the proposed model is evaluated by using the Iranian male's body type data set.

Experimental results show that the proposed model has a good accuracy and can be used as a size recommendation expert system to specify the right size for the customers. By using the proposed model and designing an interface for it, a decision support system was developed as a size recommendation expert system that was used by an apparel sales store. The results were time saving and more satisfying for the customers by selecting the appropriate apparel size for them.

In this paper, as a first study in literature, a hybrid intelligent model for developing a size recommendation expert system based on data clustering and a PNN to enable the salesperson to help the consumer in choosing the right size is proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference to develop a new intelligent classification system by using a PNN. In the last stage, the accuracy of the proposed model is evaluated by using testing data. The proposed model achieved an 87.2 percent accuracy rate that is very promising.

A framework for a hybrid intelligent support system is proposed, on the basis of a discussion of the main problems of current computer‐based support systems and the roles for computer‐based systems in developing marketing strategy. The objectives of the framework are: to integrate the strengths of different support techniques and technologies; to assist strategic analysis; to couple strategic analysis with managers’ judgement; to help managers deal with uncertainty; and to aid strategic thinking. Within this framework, the benefits of different strategic analysis models are combined to offer enhanced support for a logical sequence of strategic analysis, while the advantages of diverse support techniques and technologies are integrated and fitted to support different aspects of the marketing strategy development process. As well as the theoretical basis for the proposed framework, the paper also examines the associated technical issues.

Hybrid systems as the next generation of intelligent applications within the field of mass appraisal and valuation are investigated. Motivated by the obvious limitations of paradigms that are being used in isolation or as stand‐alone techniques such as multiple regression analysis, artificial neural networks and expert systems. Clearly, there are distinct advantages in integrating two or more information processing systems that would address some of the discrete problems of individual techniques. Examines first, the strategic development of mass appraisal approaches which have traditionally been based on “stand‐alone” techniques; second, the potential application of an intelligent hybrid system. Highlights possible solutions by investigating various hybrid systems that may be developed incorporating a nearest neighbour algorithm (k‐NN). The enhancements are aimed at two major deficiencies in traditional distance metrics; user dependence for attribute weights and biases in the distance metric towards matching categorical variables in the retrieval of neighbours. Solutions include statistical techniques: mean, coefficient of variation and significant mean. Data mining paradigms based on a loosely coupled neural network or alternatively a tight coupling with genetic algorithms are used to discover attribute weights. The hybrid architectures developed are applied to a property data set and their performance measured based on their predictive value as well as perspicuity. Concludes by considering the application and the relevance of these techniques within the field of computer assisted mass appraisal.

With the development of research toward damage detection in structural elements, the use of artificial intelligent methods for crack detection plays a vital role in solving the crack-related problems. The purpose of this paper is to establish a methodology that can detect and analyze crack development in a beam structure subjected to transverse free vibration.

Hybrid intelligent systems have acquired their own distinction as a potential problem-solving methodology adopted by researchers and scientists. It can be applied in many areas like science, technology, business and commerce. There have been the efforts by researchers in the recent past to combine the individual artificial intelligent techniques in parallel to generate optimal solutions for the problems. So it is an innovative effort to develop a strong computationally intelligent hybrid system based on different combinations of available artificial intelligence (AI) techniques.

In the present research, an integration of different AI techniques has been tested for accuracy. Theoretical, numerical and experimental investigations have been carried out using a fix-hinge aluminum beam of specified dimension in the presence and absence of cracks. The paper also gives an insight into the comparison of relative crack locations and crack depths obtained from numerical and experimental results with that of the results of the hybrid intelligent model and found to be in good agreement.

The paper covers the work to verify the accuracy of hybrid controllers in a fix-hinge beam which is very rare to find in the available literature. To overcome the limitations of standalone AI techniques, a hybrid methodology has been adopted. The output results for crack location and crack depth have been compared with experimental results, and the deviation of results is found to be within the satisfactory limit.

Explores the potential of a hybrid intelligent system in supporting marketing strategy development. First, a hybrid intelligent system for developing marketing strategy, called MarStra (developed by the author), is outlined. Then discusses the real‐world tests of MarStra with marketing directors in five large UK companies. Empirical evidence from the companies involved indicates that MarStra is very helpful and useful in: providing strategic analysis guidance; coupling strategic analysis with managerial judgement; helping strategic thinking; dealing with fuzziness and uncertainty; and supporting group assessment of strategic marketing factors. The intelligent outputs generated by MarStra were reported to be surprisingly accurate, mostly sound and useful prompts.

The purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs.

The node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA.

This study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions.

For simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered.

This research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.