At the core of artificial intelligence, machine learning and computational science lies the intriguing concept of Evolutionary Algorithms (EAs). This set of problem-solving operations draw inspiration from principles seen in natural evolution such as survival of the fittest, mutation, recombination and selection. Built to mimic the process of natural selection, these algorithms are applied to find optimal or near-optimal solutions to complex problems which otherwise may not be possible to solve using traditional techniques.

The primary characteristics of Evolutionary Algorithms include:

• No Requirement for Derivative Information: Since Evolutionary Algorithms only evaluate the cost, function, and fitness values of potential solutions, they do not require derivative information. This makes it ideal for non-differentiable, discontinuous, and multi-model problems.

• Use of a Population of Solutions: Evolutionary Algorithms operate on a population of solutions as opposed to one single point of solution. This ensures the EA maintains a diversity of solutions, avoiding stagnation at local optima.

• Parallel and Distributed Processing: EAs inherently support parallel and distributed processing. This allows them to evaluate multiple solutions simultaneously, thus accelerating the search process.

• Dynamic Adaptation: Evolutionary Algorithms can be readily employed in dynamic environments where the optimization landscape might change over time. This is particularly useful where fine-tuning and adaptability are of prime importance.

• Strong Robustness: EAs are highly flexible and robust, which means they can find feasible solutions in complex and changing conditions where other traditional methods may fail.

Effective application of Evolutionary Algorithms commences with clearly articulating the problem to be solved. The next step involves codifying potential solutions into 'chromosomes,' those are strings of binary, integer, or real-valued numbers.

Performance measure or fitness function that assigns quality value to each chromosome in context to the problem needs to be defined. Thereafter, mimicking the principles of natural evolution, the process of selection, reproduction, recombination, and mutation is repeated over many generations. This iteratively refines the population of chromosomes, driving it toward optimal or near-optimal solutions.

It's imperative to understand that selecting the right EA variant and appropriately setting its parameters is fundamental to successful implementation. Also, visualization techniques for monitoring the algorithm’s journey can be tremendously helpful for diagnosing problem areas and aiding in solution refinement.

As with any computational tool, the advantages of Evolutionary Algorithms should be weighed against its limitations. These robust algorithms have been increasingly harnessed to solve complex problems in various domains, from logistics and process control to high-frequency trading and satellite scheduling. It’s clear that as computational resources continue to proliferate, the utilization of EAs in such intricate scenarios will continue to become more prevalent and efficient.

Evolutionary algorithms have inherent advantages which make them a preferred choice for solving complex problems:

• Versatility: EAs are applicable in virtually any type of problem domain. This includes numerical optimization, discrete optimization, combinatorial optimization, constrained optimization, multi-objective optimization, and so on.

• High-quality Solutions: Even when absolute optimality can’t be guaranteed, evolutionary algorithms can still get close to the optimal solution.

• Parallelism: EAs optimize on multiple fronts instead of a single point, thus reducing the time taken to arrive at a solution.

• Scalability: Evolutionary Algorithms are scalable with problem size and complexity, which is beneficial in handling real-world problems.

• Adaptability: The adaptability of evolutionary algorithms makes them suitable for dynamic problems where the environment and conditions change over time.

Despite their strengths, several challenges and limitations persist with the use of Evolutionary Algorithms:

• Computational Cost: Evolutionary Algorithms may require high computational resources and time, particularly in large-scale and complex problem-solving.

• Premature Convergence: There may be instances where the algorithm converges prematurely to a suboptimal solution, providing a less than ideal outcome.

• Lack of Theoretical Background: Though EAs are popular, their theoretical understanding is still quite limited.