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Funnels are related to the big-valley hypothesis in combinatorial fitness landscapes. It suggests that local optima are not randomly distributed but are instead clustered around the global optimum, forming a coarse-grained global structure. Multi-...$$

Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. ...

Compressed monotonic local optima networks (CMLONs) provide a powerful means to visualise the global structure of the fitness landscapes of optimisation problems as network graphs. Historically, they have been developed for discrete optimisation ...

Fitness landscape analysis (FLA) refers to a set of techniques to characterise optimisation problems. This paper presents an FLA of three types of genetic programming (GP) benchmarks: parity, symbolic regression, and artificial ant. We applied a ...

In most of the existing benchmark generators for combinatorial optimization, one cannot tune variable importance, making the generation of real-like instances challenging. However, when it comes to the study of optimization algorithms or new problems ...

Landscape analysis is a very useful tool in optimization to understand the structure of the search space of a problem when there is some kind of distance or neighborhood defined over the solutions. Local Optima Networks (LON) have been proposed to ...

Neural architecture search (NAS) involves automatically searching for promising deep neural network structures in certain architecture spaces. Depending on the number of criteria being concerned, NAS can be formulated as single-objective optimization ...

Since its inception in 2013, the Travelling Thief Problem (TTP) has been widely studied as an example of problems with multiple interconnected sub-problems. The dependency in this model arises when tying the travelling time of the "thief" to the weight ...

A fitness landscape describes the interaction of a search domain, a cost function on designs drawn from the domain, and a neighbourhood function defining the adjacency of designs --- induced by the optimisation method used. Fitness landscapes can be ...

A major challenge with utilizing a metaheuristic is finding optimal or near optimal parameters for a given problem instance. It is well known that the best performing control parameters are often problem dependent, with poorly chosen parameters even ...

One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works for one problem, it is anticipated that it will be effective for problems ...

In local search algorithms, the pivoting rule determines which neighboring solution to select and thus strongly influences the behavior of the algorithm and its capacity to sample good-quality local optima. The classical pivoting rules are first and ...

In contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms. It is believed that this is the consequence of the non-random structure of the distribution of variables into clauses. ...

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared ...

Most algorithms proposed for solving complex problems require the definition of some parameter values. The process of finding suitable parameter values is an optimization problem by itself. Understanding the global structure of search spaces of complex ...

A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features ...

Local Optima Networks (LONs) are a valuable tool to understand fitness landscapes of optimization problems observed from the perspective of a search algorithm. Local optima of the optimization problem are linked by an edge in LONs when an operation in ...

Local Optima Networks (LONs) have been proposed as a coarsegrained model of discrete (combinatorial) fitness landscapes, where nodes are local optima and edges are search transitions based on an exploration search operator. This paper presents one of ...

Reinforcement learning is gaining prominence in the machine learning community. It dates back over three decades in areas such as cybernetics and psychology, but has more recently been applied widely in robotics, game playing and control systems. There ...

Local Optima Networks are models proposed to understand the structure and properties of combinatorial landscapes. The fitness landscape is explored as a graph whose nodes represent the local optima (or basins of attraction) and edges represent the ...