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This paper addresses issues dealing with genetic algorithm (GA) convergence and the implications of the No Free Lunch Theorem which states that no single algorithm outperforms all others for all possible problem landscapes. In view of this, the authors propose that it is necessary for a GA to have the ability to classify the problem landscape before effective parameter adaptation may occur.

The new hybrid intelligent system for landscape classification is proposed. This system facilitates intelligent operator selection and parameter tuning during run time in order to achieve maximum convergence. This work introduces two adaptive crossover techniques, the runtime adaptation of crossover probability and the participation level of multiple crossover operators in order to refine the quality of the search and to regulate the trade‐off between local and global search respectively. In addition, a Rule‐Based reasoning system (RS) is presented which can be utilised to analyse the problem landscape and provide a supervisory element to a GA. This RS is capable of instigating change by utilising the analysis in order to counteract premature convergence, for various classes of problems.

Results are presented which show that the application of this Rule‐Based system and the adaptive crossover techniques proposed in this paper significantly improve performance for a suite of relatively complex test problems.

This work demonstrates the effectiveness of landscape classification and consequent rule‐based reasoning for GAs, particularly for problems with a difficult path to the optimal. Moreover, both adaptive crossover techniques proposed present improved performance over the traditional static parameter GA.

Existing regulatory frameworks for identifying and treating historic buildings and places reflect deference to expert rule, which privileges the values of a small number of heritage experts over the values of the majority of people who visit, work, and reside in historic environments. The purpose of this paper is to explore a fundamental shift in how US federal and local preservation laws address built heritage by suggesting a dynamic, adaptive regulatory framework that incorporates heterodox approaches to heritage and therefore is capable of accommodating contemporary sociocultural values.

The overall approach used is a comparative literature review from the fields of heterodox/orthodox heritage, heterodox/orthodox law, adaptive management, and participatory methods to inform the creation of a dynamic, adaptive regulatory framework.

Tools such as dialogical democracy and participatory action research are sufficiently pragmatic in implementation to envision how an adaptive regulatory framework could be implemented. This new framework would likely require heterodox definitions of law that move beyond justice as a primary purpose and broaden the nature of legal goods that can be protected while addressing discourses of power to benefit a larger group of stakeholders.

The authors suggest that an adaptive regulatory framework would be particularly beneficial for architectural and urban conservation planning, as it foregrounds considerations other than property rights in decision-making processes. While such a goal appears to be theoretically possible, the challenge will be to translate the theory of an adaptive regulatory framework into practice as there does not appear to be any precedent for its implementation. There will be issues with the need for increased resources to implement this framework.

To date, there have been few, if any, attempts to address critical heritage studies theory in the context of the regulatory environment. This paper appears to be the first such investigation in the literature.

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

The collective intelligence emerging from behaviors of social insects has become an inspiration source that is impossible to avoid; guiding researchers in various domains to solutions of insolvent problems by traditional approaches. These behaviors are made possible because of the interactions individual‐individual and individual‐environment, representing support on which cooperative work within the same group is based and allowing emergence at macroscopic level of sophisticated achievements. Many models were inspired by this new and very promising vision, to find simple rules, leading mobile, autonomous robots with limited capacities in their environment to realize tasks, like those of: browsing, collecting or self‐assembly. In this context, the purpose of this paper is to suggest a method, making global behavior evolve within an homogeneous agent‐robots community to accomplish heap‐formation task based on appointment principle in changing environment which can be very difficult. Control device, comparable to the functioning of cellular automaton containing sensory‐motor rules, is then used to arbitrate between some given elementary attitudes with which each agent‐robot initially is equipped.

Evolutionary approach using genetic algorithm based on reverse emergence principle seeks, then, for cellular automaton whose arbitration succeeds to realize this adaptive oriented grouping task.

Rules as simulation results obtained according to reactive model of multi‐agent systems are provided, compared with those found at the ants and commented.

Discovered rules are adaptive; it means when training ground becomes more difficult, agent‐robots become more flexible by decreasing thresholds conditioning rules application. If environment state continues to turn into harsh, robots are able to seek for another direction to start new heap formation somewhere else. Such zones are like Saharan region, airports or supermarkets.

The primary objective of this paper is to discuss whether complexity science can help overcome management's dilemma of how to balance efficiency and innovation.

Complexity science provides an interdisciplinary theoretical approach for studying complex adaptive systems (CAS), which exhibit adequate combinations of both emergent efficiency and emergent innovation. Based on prominent models from complexity science, a generic framework of CAS is proposed that shows the design levers of such systems. This framework then serves to assess recent literature on applications of complexity science to firms. Applications cover a broad range of objectives and four organizational levels: the individual resource, the organizational sub‐unit (SU), the organizational, and the network levels. The generic framework is used to classify the applications' objectives in terms of efficiency and innovation, and to identify the design levers they use.

CAS offer a valuable theoretical perspective on efficiency and innovation. However, the proposed framework shows that these systems are not utilized to their full potential when applied to firms. Typical applications address either emergent efficiency or emergent innovation and thus fail to balance both.

The paper does not provide an exhaustive literature review on management applications of CAS, but selects exemplary literature.

The paper gives a comprehensive overview of the CAS' perspective in management science. For further research, the proposed generic framework of CAS may serve to analyze, evaluate and integrate applications in order to overcome the efficiency‐innovation dilemma.

Recently, the micro-positioning technology has become more important for achieving the requirement of precision machinery. The piezo-actuator plays a very important role in this application area. A model-free adaptive sliding controller with fuzzy compensation is proposed for a piezo-actuated micro-drilling process control in this paper. The paper aims to discuss these issues.

Due to the system's nonlinear and time-varying characteristics, this control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement of the sliding mode control. In addition, a fuzzy scheme with online learning ability is augmented to compensate for the finite approximation error and facilitate the controller design.

The Lyapunov direct method can be applied to find adaptive laws for updating coefficients in the approximating series and tuning parameter in the fuzzy compensator to guarantee the control system stability. With the addition adaptive fuzzy compensator, as less as five Fourier series functions can be used to approximate the nonlinear time-varying function for designing a sliding mode controller for micro-drilling process control.

The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function.

The objective of this paper is to develop geometric algorithms and planning strategies to enable the development of a novel hybrid manufacturing process, which combines rapidly re‐configurable mold tooling and multi‐axis machining.

The presented hybrid process combines advantages of both reconfigurable molding and machining processes. The mold's re‐configurability is based on the concept of using an array of discrete pins. By positioning the pins, the reconfigurable molding process allows forming the mold cavity directly from the object's 3D design model, without any human intervention. After a segment of the part is molded using the reconfigurable molding process, a multi‐axis machining operation is used to create accurate parts with better surface finish. Geometric algorithms are developed to decompose the design model into segments based on the part's moldability and machinability. The decomposed features are used for planning the reconfigurable molding and the multi‐axis machining operations.

Computer implementation and illustrative examples are also presented in this paper. The results showed that the developed algorithms enable the proposed hybrid re‐configurable molding and multi‐axis machining process. The developed decomposition and planning algorithms are used for planning the reconfigurable molding and the multi‐axis machining operations. Owing to the decomposition strategy, more geometrically complex parts can be fabricated using the developed hybrid process.

This paper presents geometric analysis and planning to enable the development of a novel hybrid manufacturing process, which combines rapidly re‐configurable mold tooling and multi‐axis machining. It is expected that the proposed hybrid manufacturing process can produce highly customized parts with better surface finish, and part accuracy, with shorter build times, and reduced setup and tooling costs.

Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.

The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.

The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.

Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

This paper introduces a conceptual lens for leading social change in slums and informal settlements. In line with this aim, the purpose of this case study is to describe the public problem-solving approach of a social change organization situated in an informal settlement through the lens of adaptive leadership, complexity theory and social change leadership (SCL).

This paper follows an engaged reflection tradition. First, the author-practitioners describe an informal settlement case hereafter called ISC in southeast Iran where many people have historically remained undocumented and uneducated. Using complex adaptive systems theory, adaptive leadership and SCL as the conceptual lens, the paper analyzes ISC as a complex adaptive context in which the community and the government are in tension in solving problems, particularly illiteracy. The instrumental case study draws from participant observation and document analysis to describe and examine the endeavors of a community office operating within ISC. Through this reflective analysis, the authors illustrate how a social change organization can effectively tackle public issues like illiteracy within informal settlements.

This paper applies complexity leadership theory to a social context. The study illustrates how social change organizations can support the transformation of informal spaces into adaptive spaces to enact social change.

This paper reflects on engagement activity near the insecure borders of Iran, Afghanistan and Pakistan. By extending an organizational-level theory to the public sphere, this paper contributes theoretically to the complexity theory literature. Moreover, it provides a practical insight for community development and slum upgrading projects.

In order for a software system to better serve the user, it should be able to adjust its behavior according to the changing needs in the environment. Oftentimes, selecting a particular action may depend upon various non‐functional requirements (NFRs) such as safety, cost, and so on. In the past, the many possible alternatives for an adaptation action by and large have not been considered systematically and rationally, keeping various NFRs in mind, hence, resulting in low‐level of confidence that such an action is indeed a best possible one that is really desirable. The purpose of this paper is to present a goal‐oriented approach to select alternative(s) based on a particular contextual event, while considering important NFRs.

The paper proposes a goal‐oriented approach in which various NFRs are treated as softgoals to be satisficed and used in exploring, analyzing and selecting among possible adaptation alternatives, in consideration of the particular contextual event.

Without the goal‐oriented methodology, which offers an ontology enriched with the notion of goals for contextual information and also integrates rules for triggering adaptation, the authors feel, through their scenario study applied to their smart‐phone application, that some critical issues might not have been considered in building a usable, useful system.

The concepts introduced in this paper provide a systematic and rational approach to select adaptation alternative(s), considering NFRs along with detecting a contextual event.